Overview

Dataset statistics

Number of variables256
Number of observations35256
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory68.9 MiB
Average record size in memory2.0 KiB

Variable types

Categorical138
Numeric117
Unsupported1

Warnings

N°Liste has constant value "1" Constant
Libellé Abrégé Liste has constant value "LA FRANCE INSOUMISE" Constant
Libellé Etendu Liste has constant value "LA FRANCE INSOUMISE" Constant
Nom Tête de Liste has constant value "AUBRY Manon" Constant
Unnamed: 25 has constant value "2" Constant
Unnamed: 26 has constant value "UNE FRANCE ROYALE" Constant
Unnamed: 27 has constant value "UNE FRANCE ROYALE AU COEUR DE L'EUROPE" Constant
Unnamed: 28 has constant value "DE PREVOISIN Robert" Constant
Unnamed: 32 has constant value "3" Constant
Unnamed: 33 has constant value "LA LIGNE CLAIRE" Constant
Unnamed: 34 has constant value "LA LIGNE CLAIRE" Constant
Unnamed: 35 has constant value "CAMUS Renaud" Constant
Unnamed: 39 has constant value "4" Constant
Unnamed: 40 has constant value "PARTI PIRATE" Constant
Unnamed: 41 has constant value "PARTI PIRATE" Constant
Unnamed: 42 has constant value "MARIE Florie" Constant
Unnamed: 46 has constant value "5" Constant
Unnamed: 47 has constant value "RENAISSANCE" Constant
Unnamed: 48 has constant value "RENAISSANCE SOUTENUE PAR LA RÉPUBLIQUE EN MARCHE, LE MODEM ET SES PARTENAIRES" Constant
Unnamed: 49 has constant value "LOISEAU Nathalie" Constant
Unnamed: 53 has constant value "6" Constant
Unnamed: 54 has constant value "DÉMOCRATIE REPRÉSENTATIVE" Constant
Unnamed: 55 has constant value "DÉMOCRATIE REPRÉSENTATIVE" Constant
Unnamed: 56 has constant value "TRAORÉ Hamada" Constant
Unnamed: 60 has constant value "7" Constant
Unnamed: 61 has constant value "ENSEMBLE PATRIOTES" Constant
Unnamed: 62 has constant value "ENSEMBLE PATRIOTES ET GILETS JAUNES : POUR LA FRANCE, SORTONS DE L'UNION EUROPÉENNE !" Constant
Unnamed: 63 has constant value "PHILIPPOT Florian" Constant
Unnamed: 67 has constant value "8" Constant
Unnamed: 68 has constant value "PACE" Constant
Unnamed: 69 has constant value "PACE - PARTI DES CITOYENS EUROPÉENS" Constant
Unnamed: 70 has constant value "ALEXANDRE Audric" Constant
Unnamed: 74 has constant value "9" Constant
Unnamed: 75 has constant value "URGENCE ÉCOLOGIE" Constant
Unnamed: 76 has constant value "URGENCE ÉCOLOGIE" Constant
Unnamed: 77 has constant value "BOURG Dominique" Constant
Unnamed: 81 has constant value "10" Constant
Unnamed: 82 has constant value "LISTE DE LA RECONQUÊTE" Constant
Unnamed: 83 has constant value "LISTE DE LA RECONQUÊTE" Constant
Unnamed: 84 has constant value "VAUCLIN Vincent" Constant
Unnamed: 88 has constant value "11" Constant
Unnamed: 89 has constant value "LES EUROPÉENS" Constant
Unnamed: 90 has constant value "LES EUROPÉENS" Constant
Unnamed: 91 has constant value "LAGARDE Jean-Christophe" Constant
Unnamed: 95 has constant value "12" Constant
Unnamed: 96 has constant value "ENVIE D'EUROPE" Constant
Unnamed: 97 has constant value "ENVIE D'EUROPE ÉCOLOGIQUE ET SOCIALE" Constant
Unnamed: 98 has constant value "GLUCKSMANN Raphaël" Constant
Unnamed: 102 has constant value "13" Constant
Unnamed: 103 has constant value "PARTI FED. EUROPÉEN" Constant
Unnamed: 104 has constant value "PARTI FÉDÉRALISTE EUROPÉEN - POUR UNE EUROPE QUI PROTÈGE SES CITOYENS" Constant
Unnamed: 105 has constant value "GERNIGON Yves" Constant
Unnamed: 109 has constant value "14" Constant
Unnamed: 110 has constant value "INITIATIVE CITOYENNE" Constant
Unnamed: 111 has constant value "MOUVEMENT POUR L'INITIATIVE CITOYENNE" Constant
Unnamed: 112 has constant value "HELGEN Gilles" Constant
Unnamed: 116 has constant value "15" Constant
Unnamed: 117 has constant value "DEBOUT LA FRANCE" Constant
Unnamed: 118 has constant value "LE COURAGE DE DÉFENDRE LES FRANÇAIS AVEC NICOLAS DUPONT-AIGNAN. DEBOUT LA FRANCE ! - CNIP" Constant
Unnamed: 119 has constant value "DUPONT-AIGNAN Nicolas" Constant
Unnamed: 123 has constant value "16" Constant
Unnamed: 124 has constant value "ALLONS ENFANTS" Constant
Unnamed: 125 has constant value "ALLONS ENFANTS" Constant
Unnamed: 126 has constant value "CAILLAUD Sophie" Constant
Unnamed: 130 has constant value "17" Constant
Unnamed: 131 has constant value "DÉCROISSANCE 2019" Constant
Unnamed: 132 has constant value "DÉCROISSANCE 2019" Constant
Unnamed: 133 has constant value "DELFEL Thérèse" Constant
Unnamed: 137 has constant value "18" Constant
Unnamed: 138 has constant value "LUTTE OUVRIÈRE" Constant
Unnamed: 139 has constant value "LUTTE OUVRIÈRE - CONTRE LE GRAND CAPITAL, LE CAMP DES TRAVAILLEURS" Constant
Unnamed: 140 has constant value "ARTHAUD Nathalie" Constant
Unnamed: 144 has constant value "19" Constant
Unnamed: 145 has constant value "POUR L'EUROPE DES GENS" Constant
Unnamed: 146 has constant value "POUR L'EUROPE DES GENS CONTRE L'EUROPE DE L'ARGENT" Constant
Unnamed: 147 has constant value "BROSSAT Ian" Constant
Unnamed: 151 has constant value "20" Constant
Unnamed: 152 has constant value "ENSEMBLE POUR LE FREXIT" Constant
Unnamed: 153 has constant value "ENSEMBLE POUR LE FREXIT" Constant
Unnamed: 154 has constant value "ASSELINEAU François" Constant
Unnamed: 158 has constant value "21" Constant
Unnamed: 159 has constant value "LISTE CITOYENNE" Constant
Unnamed: 160 has constant value "LISTE CITOYENNE DU PRINTEMPS EUROPÉEN AVEC BENOÎT HAMON SOUTENUE PAR GÉNÉRATION.S ET DÈME-DIEM 25" Constant
Unnamed: 161 has constant value "HAMON Benoît" Constant
Unnamed: 165 has constant value "22" Constant
Unnamed: 166 has constant value "À VOIX ÉGALES" Constant
Unnamed: 167 has constant value "À VOIX ÉGALES" Constant
Unnamed: 168 has constant value "TOMASINI Nathalie" Constant
Unnamed: 172 has constant value "23" Constant
Unnamed: 173 has constant value "PRENEZ LE POUVOIR" Constant
Unnamed: 174 has constant value "PRENEZ LE POUVOIR, LISTE SOUTENUE PAR MARINE LE PEN" Constant
Unnamed: 175 has constant value "BARDELLA Jordan" Constant
Unnamed: 179 has constant value "24" Constant
Unnamed: 180 has constant value "NEUTRE ET ACTIF" Constant
Unnamed: 181 has constant value "NEUTRE ET ACTIF" Constant
Unnamed: 182 has constant value "CORBET Cathy Denise Ginette" Constant
Unnamed: 186 has constant value "25" Constant
Unnamed: 187 has constant value "RÉVOLUTIONNAIRE" Constant
Unnamed: 188 has constant value "PARTI RÉVOLUTIONNAIRE COMMUNISTES" Constant
Unnamed: 189 has constant value "SANCHEZ Antonio" Constant
Unnamed: 193 has constant value "26" Constant
Unnamed: 194 has constant value "ESPERANTO" Constant
Unnamed: 195 has constant value "ESPÉRANTO - LANGUE COMMUNE ÉQUITABLE POUR L'EUROPE" Constant
Unnamed: 196 has constant value "DIEUMEGARD Pierre" Constant
Unnamed: 200 has constant value "27" Constant
Unnamed: 201 has constant value "ÉVOLUTION CITOYENNE" Constant
Unnamed: 202 has constant value "ÉVOLUTION CITOYENNE" Constant
Unnamed: 203 has constant value "CHALENÇON Christophe" Constant
Unnamed: 207 has constant value "28" Constant
Unnamed: 208 has constant value "ALLIANCE JAUNE" Constant
Unnamed: 209 has constant value "ALLIANCE JAUNE, LA RÉVOLTE PAR LE VOTE" Constant
Unnamed: 210 has constant value "LALANNE Francis" Constant
Unnamed: 214 has constant value "29" Constant
Unnamed: 215 has constant value "UNION DROITE-CENTRE" Constant
Unnamed: 216 has constant value "UNION DE LA DROITE ET DU CENTRE" Constant
Unnamed: 217 has constant value "BELLAMY François-Xavier" Constant
Unnamed: 221 has constant value "30" Constant
Unnamed: 222 has constant value "EUROPE ÉCOLOGIE" Constant
Unnamed: 223 has constant value "EUROPE ÉCOLOGIE" Constant
Unnamed: 224 has constant value "JADOT Yannick" Constant
Unnamed: 228 has constant value "31" Constant
Unnamed: 229 has constant value "PARTI ANIMALISTE" Constant
Unnamed: 230 has constant value "PARTI ANIMALISTE" Constant
Unnamed: 231 has constant value "THOUY Hélène" Constant
Unnamed: 235 has constant value "32" Constant
Unnamed: 236 has constant value "LES OUBLIES DE L'EUROPE" Constant
Unnamed: 237 has constant value "LES OUBLIÉS DE L'EUROPE - ARTISANS, COMMERÇANTS, PROFESSIONS LIBÉRALES ET INDÉPENDANTS - ACPLI -" Constant
Unnamed: 238 has constant value "BIDOU Olivier" Constant
Unnamed: 242 has constant value "33" Constant
Unnamed: 243 has constant value "UDLEF" Constant
Unnamed: 244 has constant value "UDLEF (UNION DÉMOCRATIQUE POUR LA LIBERTÉ ÉGALITÉ FRATERNITÉ)" Constant
Unnamed: 245 has constant value "PERSON Christian Luc" Constant
Unnamed: 249 has constant value "34" Constant
Unnamed: 250 has constant value "EUROPE AU SERVICE PEUPLES" Constant
Unnamed: 251 has constant value "UNE EUROPE AU SERVICE DES PEUPLES" Constant
Unnamed: 252 has constant value "AZERGUI Nagib" Constant
Code du département has a high cardinality: 107 distinct values High cardinality
Libellé du département has a high cardinality: 107 distinct values High cardinality
Inscrits is highly correlated with Abstentions and 19 other fieldsHigh correlation
Abstentions is highly correlated with Inscrits and 17 other fieldsHigh correlation
% Abs/Ins is highly correlated with % Vot/Ins and 1 other fieldsHigh correlation
Votants is highly correlated with Inscrits and 17 other fieldsHigh correlation
% Vot/Ins is highly correlated with % Abs/Ins and 1 other fieldsHigh correlation
Blancs is highly correlated with Inscrits and 6 other fieldsHigh correlation
% Blancs/Ins is highly correlated with % Blancs/VotHigh correlation
% Blancs/Vot is highly correlated with % Blancs/InsHigh correlation
Nuls is highly correlated with Abstentions and 1 other fieldsHigh correlation
% Nuls/Ins is highly correlated with % Nuls/VotHigh correlation
% Nuls/Vot is highly correlated with % Nuls/InsHigh correlation
Exprimés is highly correlated with Inscrits and 17 other fieldsHigh correlation
% Exp/Ins is highly correlated with % Abs/Ins and 1 other fieldsHigh correlation
Voix is highly correlated with Inscrits and 17 other fieldsHigh correlation
% Voix/Ins is highly correlated with % Voix/ExpHigh correlation
% Voix/Exp is highly correlated with % Voix/InsHigh correlation
Unnamed: 30 is highly correlated with Unnamed: 31High correlation
Unnamed: 31 is highly correlated with Unnamed: 30High correlation
Unnamed: 37 is highly correlated with Unnamed: 38High correlation
Unnamed: 38 is highly correlated with Unnamed: 37High correlation
Unnamed: 43 is highly correlated with Inscrits and 11 other fieldsHigh correlation
Unnamed: 44 is highly correlated with Unnamed: 45High correlation
Unnamed: 45 is highly correlated with Unnamed: 44High correlation
Unnamed: 50 is highly correlated with Inscrits and 16 other fieldsHigh correlation
Unnamed: 58 is highly correlated with Unnamed: 59High correlation
Unnamed: 59 is highly correlated with Unnamed: 58High correlation
Unnamed: 64 is highly correlated with Unnamed: 120 and 2 other fieldsHigh correlation
Unnamed: 65 is highly correlated with Unnamed: 66High correlation
Unnamed: 66 is highly correlated with Unnamed: 65High correlation
Unnamed: 72 is highly correlated with Unnamed: 73High correlation
Unnamed: 73 is highly correlated with Unnamed: 72High correlation
Unnamed: 78 is highly correlated with Inscrits and 18 other fieldsHigh correlation
Unnamed: 79 is highly correlated with Unnamed: 80High correlation
Unnamed: 80 is highly correlated with Unnamed: 79High correlation
Unnamed: 86 is highly correlated with Unnamed: 87High correlation
Unnamed: 87 is highly correlated with Unnamed: 86High correlation
Unnamed: 92 is highly correlated with Inscrits and 12 other fieldsHigh correlation
Unnamed: 93 is highly correlated with Unnamed: 94High correlation
Unnamed: 94 is highly correlated with Unnamed: 93High correlation
Unnamed: 99 is highly correlated with Inscrits and 15 other fieldsHigh correlation
Unnamed: 100 is highly correlated with Unnamed: 101High correlation
Unnamed: 101 is highly correlated with Unnamed: 100High correlation
Unnamed: 107 is highly correlated with Unnamed: 108High correlation
Unnamed: 108 is highly correlated with Unnamed: 107High correlation
Unnamed: 114 is highly correlated with Unnamed: 115High correlation
Unnamed: 115 is highly correlated with Unnamed: 114High correlation
Unnamed: 120 is highly correlated with Inscrits and 12 other fieldsHigh correlation
Unnamed: 121 is highly correlated with Unnamed: 122High correlation
Unnamed: 122 is highly correlated with Unnamed: 121High correlation
Unnamed: 127 is highly correlated with Unnamed: 50High correlation
Unnamed: 128 is highly correlated with Unnamed: 129High correlation
Unnamed: 129 is highly correlated with Unnamed: 128High correlation
Unnamed: 135 is highly correlated with Unnamed: 136High correlation
Unnamed: 136 is highly correlated with Unnamed: 135High correlation
Unnamed: 141 is highly correlated with Inscrits and 11 other fieldsHigh correlation
Unnamed: 142 is highly correlated with Unnamed: 143High correlation
Unnamed: 143 is highly correlated with Unnamed: 142High correlation
Unnamed: 148 is highly correlated with Inscrits and 13 other fieldsHigh correlation
Unnamed: 149 is highly correlated with Unnamed: 150High correlation
Unnamed: 150 is highly correlated with Unnamed: 149High correlation
Unnamed: 155 is highly correlated with Inscrits and 17 other fieldsHigh correlation
Unnamed: 156 is highly correlated with Unnamed: 157High correlation
Unnamed: 157 is highly correlated with Unnamed: 156High correlation
Unnamed: 162 is highly correlated with Inscrits and 15 other fieldsHigh correlation
Unnamed: 163 is highly correlated with Unnamed: 164High correlation
Unnamed: 164 is highly correlated with Unnamed: 163High correlation
Unnamed: 169 is highly correlated with Unnamed: 50High correlation
Unnamed: 170 is highly correlated with Unnamed: 171High correlation
Unnamed: 171 is highly correlated with Unnamed: 170High correlation
Unnamed: 176 is highly correlated with Unnamed: 64 and 3 other fieldsHigh correlation
Unnamed: 177 is highly correlated with Unnamed: 178High correlation
Unnamed: 178 is highly correlated with Unnamed: 177High correlation
Unnamed: 184 is highly correlated with Unnamed: 185High correlation
Unnamed: 185 is highly correlated with Unnamed: 184High correlation
Unnamed: 191 is highly correlated with Unnamed: 192High correlation
Unnamed: 192 is highly correlated with Unnamed: 191High correlation
Unnamed: 197 is highly correlated with Inscrits and 4 other fieldsHigh correlation
Unnamed: 198 is highly correlated with Unnamed: 199High correlation
Unnamed: 199 is highly correlated with Unnamed: 198High correlation
Unnamed: 205 is highly correlated with Unnamed: 206High correlation
Unnamed: 206 is highly correlated with Unnamed: 205High correlation
Unnamed: 211 is highly correlated with Unnamed: 64 and 3 other fieldsHigh correlation
Unnamed: 212 is highly correlated with Unnamed: 213High correlation
Unnamed: 213 is highly correlated with Unnamed: 212High correlation
Unnamed: 218 is highly correlated with Inscrits and 14 other fieldsHigh correlation
Unnamed: 219 is highly correlated with Unnamed: 220High correlation
Unnamed: 220 is highly correlated with Unnamed: 219High correlation
Unnamed: 225 is highly correlated with Inscrits and 14 other fieldsHigh correlation
Unnamed: 226 is highly correlated with Unnamed: 227High correlation
Unnamed: 227 is highly correlated with Unnamed: 226High correlation
Unnamed: 232 is highly correlated with Inscrits and 19 other fieldsHigh correlation
Unnamed: 233 is highly correlated with Unnamed: 234High correlation
Unnamed: 234 is highly correlated with Unnamed: 233High correlation
Unnamed: 239 is highly correlated with Inscrits and 4 other fieldsHigh correlation
Unnamed: 240 is highly correlated with Unnamed: 241High correlation
Unnamed: 241 is highly correlated with Unnamed: 240High correlation
Unnamed: 247 is highly correlated with Unnamed: 248High correlation
Unnamed: 248 is highly correlated with Unnamed: 247High correlation
Unnamed: 254 is highly correlated with Unnamed: 255High correlation
Unnamed: 255 is highly correlated with Unnamed: 254High correlation
Unnamed: 208 is highly correlated with Unnamed: 223 and 134 other fieldsHigh correlation
Unnamed: 223 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 27 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 175 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 237 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 75 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 74 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 245 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 193 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 202 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 216 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 95 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 172 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 68 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 174 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 207 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 196 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 116 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 165 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 125 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 124 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 180 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 235 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 97 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 132 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 90 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Libellé Etendu Liste is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 189 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 244 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 118 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 25 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Nom Tête de Liste is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 250 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 96 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 83 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 123 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 63 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 224 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 62 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Libellé Abrégé Liste is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 60 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 230 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 146 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 89 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
N°Liste is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 228 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 139 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 182 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 112 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 84 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 160 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 117 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 161 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 42 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 54 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 203 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 56 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 168 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 158 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 119 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 55 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 91 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 34 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 251 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 130 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 35 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 61 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 249 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 109 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 236 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 153 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 214 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 147 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 137 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 200 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 88 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 70 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 144 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 151 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 138 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 46 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 229 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 159 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 67 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 167 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 69 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 173 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 154 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 231 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 77 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 98 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 181 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 209 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 47 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 102 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 41 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 105 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 53 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 49 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 242 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 111 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 210 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 186 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 145 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 48 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 82 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 104 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 217 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 252 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 39 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 110 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 131 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 222 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 243 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 81 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 166 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 201 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 238 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 33 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 152 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 40 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 26 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 126 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 221 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 188 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 195 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 103 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 32 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 133 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 179 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 140 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 28 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 187 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 215 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 194 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Unnamed: 76 is highly correlated with Unnamed: 208 and 134 other fieldsHigh correlation
Inscrits is highly skewed (γ1 = 96.47532146) Skewed
Abstentions is highly skewed (γ1 = 74.49258578) Skewed
Votants is highly skewed (γ1 = 114.5210326) Skewed
Blancs is highly skewed (γ1 = 49.67641847) Skewed
Nuls is highly skewed (γ1 = 52.51286955) Skewed
Exprimés is highly skewed (γ1 = 115.647076) Skewed
Voix is highly skewed (γ1 = 87.84448745) Skewed
Unnamed: 29 is highly skewed (γ1 = 45.40257569) Skewed
Unnamed: 30 is highly skewed (γ1 = 48.91701411) Skewed
Unnamed: 31 is highly skewed (γ1 = 33.38518794) Skewed
Unnamed: 36 is highly skewed (γ1 = 42.23300978) Skewed
Unnamed: 37 is highly skewed (γ1 = 62.77599725) Skewed
Unnamed: 38 is highly skewed (γ1 = 60.37475468) Skewed
Unnamed: 43 is highly skewed (γ1 = 131.3630004) Skewed
Unnamed: 50 is highly skewed (γ1 = 137.9252363) Skewed
Unnamed: 57 is highly skewed (γ1 = 108.3695856) Skewed
Unnamed: 58 is highly skewed (γ1 = 59.54510828) Skewed
Unnamed: 59 is highly skewed (γ1 = 69.84504059) Skewed
Unnamed: 64 is highly skewed (γ1 = 45.01829669) Skewed
Unnamed: 71 is highly skewed (γ1 = 80.47056907) Skewed
Unnamed: 72 is highly skewed (γ1 = 28.23330732) Skewed
Unnamed: 73 is highly skewed (γ1 = 30.18692025) Skewed
Unnamed: 78 is highly skewed (γ1 = 98.04685896) Skewed
Unnamed: 85 is highly skewed (γ1 = 41.09332256) Skewed
Unnamed: 86 is highly skewed (γ1 = 63.96032701) Skewed
Unnamed: 87 is highly skewed (γ1 = 42.42376736) Skewed
Unnamed: 92 is highly skewed (γ1 = 87.96241213) Skewed
Unnamed: 99 is highly skewed (γ1 = 126.6568727) Skewed
Unnamed: 106 is highly skewed (γ1 = 57.69518679) Skewed
Unnamed: 113 is highly skewed (γ1 = 96.50815185) Skewed
Unnamed: 114 is highly skewed (γ1 = 28.95317533) Skewed
Unnamed: 115 is highly skewed (γ1 = 28.48818072) Skewed
Unnamed: 120 is highly skewed (γ1 = 56.10792269) Skewed
Unnamed: 127 is highly skewed (γ1 = 145.572202) Skewed
Unnamed: 128 is highly skewed (γ1 = 30.82745031) Skewed
Unnamed: 129 is highly skewed (γ1 = 22.69847299) Skewed
Unnamed: 134 is highly skewed (γ1 = 67.67045589) Skewed
Unnamed: 141 is highly skewed (γ1 = 65.93600168) Skewed
Unnamed: 148 is highly skewed (γ1 = 119.2670946) Skewed
Unnamed: 155 is highly skewed (γ1 = 97.33916481) Skewed
Unnamed: 162 is highly skewed (γ1 = 125.8328506) Skewed
Unnamed: 169 is highly skewed (γ1 = 136.6421583) Skewed
Unnamed: 170 is highly skewed (γ1 = 20.60114912) Skewed
Unnamed: 176 is highly skewed (γ1 = 50.78746086) Skewed
Unnamed: 183 is highly skewed (γ1 = 39.53230287) Skewed
Unnamed: 184 is highly skewed (γ1 = 32.32300812) Skewed
Unnamed: 185 is highly skewed (γ1 = 32.36510331) Skewed
Unnamed: 190 is highly skewed (γ1 = 56.74456778) Skewed
Unnamed: 191 is highly skewed (γ1 = 134.3318034) Skewed
Unnamed: 192 is highly skewed (γ1 = 132.2116957) Skewed
Unnamed: 197 is highly skewed (γ1 = 60.67898278) Skewed
Unnamed: 204 is highly skewed (γ1 = 27.69953719) Skewed
Unnamed: 205 is highly skewed (γ1 = 28.6470902) Skewed
Unnamed: 206 is highly skewed (γ1 = 25.80643204) Skewed
Unnamed: 211 is highly skewed (γ1 = 38.97200489) Skewed
Unnamed: 218 is highly skewed (γ1 = 124.4482709) Skewed
Unnamed: 225 is highly skewed (γ1 = 129.1496994) Skewed
Unnamed: 232 is highly skewed (γ1 = 75.737328) Skewed
Unnamed: 239 is highly skewed (γ1 = 47.7505561) Skewed
Unnamed: 246 is highly skewed (γ1 = 91.49805916) Skewed
Unnamed: 247 is highly skewed (γ1 = 28.23463912) Skewed
Unnamed: 248 is highly skewed (γ1 = 27.32641428) Skewed
Unnamed: 253 is highly skewed (γ1 = 56.59196358) Skewed
Unnamed: 255 is highly skewed (γ1 = 22.94400228) Skewed
Libellé de la commune is an unsupported type, check if it needs cleaning or further analysis Unsupported
Blancs has 2730 (7.7%) zeros Zeros
% Blancs/Ins has 2730 (7.7%) zeros Zeros
% Blancs/Vot has 2730 (7.7%) zeros Zeros
Nuls has 3067 (8.7%) zeros Zeros
% Nuls/Ins has 3067 (8.7%) zeros Zeros
% Nuls/Vot has 3067 (8.7%) zeros Zeros
Voix has 1406 (4.0%) zeros Zeros
% Voix/Ins has 1406 (4.0%) zeros Zeros
% Voix/Exp has 1406 (4.0%) zeros Zeros
Unnamed: 29 has 33892 (96.1%) zeros Zeros
Unnamed: 30 has 33978 (96.4%) zeros Zeros
Unnamed: 31 has 33901 (96.2%) zeros Zeros
Unnamed: 36 has 34839 (98.8%) zeros Zeros
Unnamed: 37 has 34886 (99.0%) zeros Zeros
Unnamed: 38 has 34859 (98.9%) zeros Zeros
Unnamed: 43 has 29709 (84.3%) zeros Zeros
Unnamed: 44 has 29721 (84.3%) zeros Zeros
Unnamed: 45 has 29709 (84.3%) zeros Zeros
Unnamed: 57 has 34697 (98.4%) zeros Zeros
Unnamed: 58 has 34760 (98.6%) zeros Zeros
Unnamed: 59 has 34718 (98.5%) zeros Zeros
Unnamed: 64 has 11587 (32.9%) zeros Zeros
Unnamed: 65 has 11590 (32.9%) zeros Zeros
Unnamed: 66 has 11587 (32.9%) zeros Zeros
Unnamed: 71 has 32323 (91.7%) zeros Zeros
Unnamed: 72 has 32369 (91.8%) zeros Zeros
Unnamed: 73 has 32328 (91.7%) zeros Zeros
Unnamed: 78 has 7093 (20.1%) zeros Zeros
Unnamed: 79 has 7093 (20.1%) zeros Zeros
Unnamed: 80 has 7093 (20.1%) zeros Zeros
Unnamed: 85 has 33866 (96.1%) zeros Zeros
Unnamed: 86 has 33928 (96.2%) zeros Zeros
Unnamed: 87 has 33875 (96.1%) zeros Zeros
Unnamed: 92 has 3938 (11.2%) zeros Zeros
Unnamed: 93 has 3938 (11.2%) zeros Zeros
Unnamed: 94 has 3938 (11.2%) zeros Zeros
Unnamed: 99 has 1963 (5.6%) zeros Zeros
Unnamed: 100 has 1963 (5.6%) zeros Zeros
Unnamed: 101 has 1963 (5.6%) zeros Zeros
Unnamed: 106 has 31209 (88.5%) zeros Zeros
Unnamed: 107 has 31252 (88.6%) zeros Zeros
Unnamed: 108 has 31213 (88.5%) zeros Zeros
Unnamed: 113 has 34102 (96.7%) zeros Zeros
Unnamed: 114 has 34166 (96.9%) zeros Zeros
Unnamed: 115 has 34114 (96.8%) zeros Zeros
Unnamed: 120 has 1802 (5.1%) zeros Zeros
Unnamed: 121 has 1802 (5.1%) zeros Zeros
Unnamed: 122 has 1802 (5.1%) zeros Zeros
Unnamed: 127 has 32282 (91.6%) zeros Zeros
Unnamed: 128 has 32317 (91.7%) zeros Zeros
Unnamed: 129 has 32286 (91.6%) zeros Zeros
Unnamed: 134 has 30724 (87.1%) zeros Zeros
Unnamed: 135 has 30760 (87.2%) zeros Zeros
Unnamed: 136 has 30727 (87.2%) zeros Zeros
Unnamed: 141 has 10532 (29.9%) zeros Zeros
Unnamed: 142 has 10532 (29.9%) zeros Zeros
Unnamed: 143 has 10532 (29.9%) zeros Zeros
Unnamed: 148 has 6048 (17.2%) zeros Zeros
Unnamed: 149 has 6048 (17.2%) zeros Zeros
Unnamed: 150 has 6048 (17.2%) zeros Zeros
Unnamed: 155 has 9403 (26.7%) zeros Zeros
Unnamed: 156 has 9403 (26.7%) zeros Zeros
Unnamed: 157 has 9403 (26.7%) zeros Zeros
Unnamed: 162 has 3838 (10.9%) zeros Zeros
Unnamed: 163 has 3838 (10.9%) zeros Zeros
Unnamed: 164 has 3838 (10.9%) zeros Zeros
Unnamed: 169 has 33044 (93.7%) zeros Zeros
Unnamed: 170 has 33105 (93.9%) zeros Zeros
Unnamed: 171 has 33053 (93.8%) zeros Zeros
Unnamed: 183 has 34939 (99.1%) zeros Zeros
Unnamed: 184 has 34970 (99.2%) zeros Zeros
Unnamed: 185 has 34949 (99.1%) zeros Zeros
Unnamed: 190 has 35027 (99.4%) zeros Zeros
Unnamed: 191 has 35048 (99.4%) zeros Zeros
Unnamed: 192 has 35033 (99.4%) zeros Zeros
Unnamed: 197 has 26822 (76.1%) zeros Zeros
Unnamed: 198 has 26833 (76.1%) zeros Zeros
Unnamed: 199 has 26824 (76.1%) zeros Zeros
Unnamed: 204 has 34215 (97.0%) zeros Zeros
Unnamed: 205 has 34281 (97.2%) zeros Zeros
Unnamed: 206 has 34231 (97.1%) zeros Zeros
Unnamed: 211 has 13110 (37.2%) zeros Zeros
Unnamed: 212 has 13114 (37.2%) zeros Zeros
Unnamed: 213 has 13110 (37.2%) zeros Zeros
Unnamed: 218 has 591 (1.7%) zeros Zeros
Unnamed: 219 has 591 (1.7%) zeros Zeros
Unnamed: 220 has 591 (1.7%) zeros Zeros
Unnamed: 225 has 545 (1.5%) zeros Zeros
Unnamed: 226 has 545 (1.5%) zeros Zeros
Unnamed: 227 has 545 (1.5%) zeros Zeros
Unnamed: 232 has 4723 (13.4%) zeros Zeros
Unnamed: 233 has 4724 (13.4%) zeros Zeros
Unnamed: 234 has 4723 (13.4%) zeros Zeros
Unnamed: 239 has 19198 (54.5%) zeros Zeros
Unnamed: 240 has 19205 (54.5%) zeros Zeros
Unnamed: 241 has 19198 (54.5%) zeros Zeros
Unnamed: 246 has 34291 (97.3%) zeros Zeros
Unnamed: 247 has 34333 (97.4%) zeros Zeros
Unnamed: 248 has 34309 (97.3%) zeros Zeros
Unnamed: 253 has 33614 (95.3%) zeros Zeros
Unnamed: 254 has 33633 (95.4%) zeros Zeros
Unnamed: 255 has 33616 (95.3%) zeros Zeros

Reproduction

Analysis started2021-02-18 21:22:49.380649
Analysis finished2021-02-18 21:26:16.393849
Duration3 minutes and 27.01 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

Code du département
Categorical

HIGH CARDINALITY

Distinct107
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
62
 
890
02
 
800
80
 
772
57
 
725
76
 
708
Other values (102)
31361 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters70512
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row01
2nd row01
3rd row01
4th row01
5th row01
ValueCountFrequency (%)
62890
 
2.5%
02800
 
2.3%
80772
 
2.2%
57725
 
2.1%
76708
 
2.0%
21698
 
2.0%
60679
 
1.9%
59648
 
1.8%
51613
 
1.7%
54591
 
1.7%
Other values (97)28132
79.8%
2021-02-18T22:26:16.572990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
62890
 
2.5%
02800
 
2.3%
80772
 
2.2%
57725
 
2.1%
76708
 
2.0%
21698
 
2.0%
60679
 
1.9%
59648
 
1.8%
51613
 
1.7%
54591
 
1.7%
Other values (97)28132
79.8%

Most occurring characters

ValueCountFrequency (%)
28449
12.0%
17885
11.2%
57787
11.0%
67703
10.9%
77656
10.9%
06790
9.6%
36783
9.6%
86440
9.1%
46060
8.6%
93751
5.3%
Other values (11)1208
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number69304
98.3%
Uppercase Letter1208
 
1.7%

Most frequent character per category

ValueCountFrequency (%)
Z633
52.4%
B270
22.4%
A156
 
12.9%
P48
 
4.0%
N33
 
2.7%
D24
 
2.0%
C22
 
1.8%
M17
 
1.4%
S2
 
0.2%
X2
 
0.2%
ValueCountFrequency (%)
28449
12.2%
17885
11.4%
57787
11.2%
67703
11.1%
77656
11.0%
06790
9.8%
36783
9.8%
86440
9.3%
46060
8.7%
93751
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common69304
98.3%
Latin1208
 
1.7%

Most frequent character per script

ValueCountFrequency (%)
Z633
52.4%
B270
22.4%
A156
 
12.9%
P48
 
4.0%
N33
 
2.7%
D24
 
2.0%
C22
 
1.8%
M17
 
1.4%
S2
 
0.2%
X2
 
0.2%
ValueCountFrequency (%)
28449
12.2%
17885
11.4%
57787
11.2%
67703
11.1%
77656
11.0%
06790
9.8%
36783
9.8%
86440
9.3%
46060
8.7%
93751
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII70512
100.0%

Most frequent character per block

ValueCountFrequency (%)
28449
12.0%
17885
11.2%
57787
11.0%
67703
10.9%
77656
10.9%
06790
9.6%
36783
9.6%
86440
9.1%
46060
8.6%
93751
5.3%
Other values (11)1208
 
1.7%

Libellé du département
Categorical

HIGH CARDINALITY

Distinct107
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
Pas-de-Calais
 
890
Aisne
 
800
Somme
 
772
Moselle
 
725
Seine-Maritime
 
708
Other values (102)
31361 

Length

Max length31
Median length7
Mean length8.754424779
Min length3

Characters and Unicode

Total characters308646
Distinct characters52
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowAin
2nd rowAin
3rd rowAin
4th rowAin
5th rowAin
ValueCountFrequency (%)
Pas-de-Calais890
 
2.5%
Aisne800
 
2.3%
Somme772
 
2.2%
Moselle725
 
2.1%
Seine-Maritime708
 
2.0%
Côte-d'Or698
 
2.0%
Oise679
 
1.9%
Nord648
 
1.8%
Marne613
 
1.7%
Meurthe-et-Moselle591
 
1.7%
Other values (97)28132
79.8%
2021-02-18T22:26:16.778252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pas-de-calais890
 
2.4%
aisne800
 
2.2%
somme772
 
2.1%
moselle725
 
2.0%
seine-maritime708
 
1.9%
côte-d'or698
 
1.9%
oise679
 
1.9%
nord648
 
1.8%
marne613
 
1.7%
meurthe-et-moselle591
 
1.6%
Other values (106)29244
80.4%

Most occurring characters

ValueCountFrequency (%)
e50334
16.3%
r24149
 
7.8%
n21479
 
7.0%
a21477
 
7.0%
-19087
 
6.2%
i16864
 
5.5%
s16174
 
5.2%
t15581
 
5.0%
o14139
 
4.6%
u10754
 
3.5%
Other values (42)98608
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter238006
77.1%
Uppercase Letter49209
 
15.9%
Dash Punctuation19087
 
6.2%
Other Punctuation1232
 
0.4%
Space Separator1112
 
0.4%

Most frequent character per category

ValueCountFrequency (%)
e50334
21.1%
r24149
10.1%
n21479
9.0%
a21477
9.0%
i16864
 
7.1%
s16174
 
6.8%
t15581
 
6.5%
o14139
 
5.9%
u10754
 
4.5%
l8908
 
3.7%
Other values (17)38147
16.0%
ValueCountFrequency (%)
M6497
13.2%
A5394
11.0%
C5021
10.2%
S4499
9.1%
H4091
8.3%
L3893
 
7.9%
P2844
 
5.8%
G2501
 
5.1%
D2202
 
4.5%
O2172
 
4.4%
Other values (11)10095
20.5%
ValueCountFrequency (%)
'1230
99.8%
/2
 
0.2%
ValueCountFrequency (%)
-19087
100.0%
ValueCountFrequency (%)
1112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin287215
93.1%
Common21431
 
6.9%

Most frequent character per script

ValueCountFrequency (%)
e50334
17.5%
r24149
 
8.4%
n21479
 
7.5%
a21477
 
7.5%
i16864
 
5.9%
s16174
 
5.6%
t15581
 
5.4%
o14139
 
4.9%
u10754
 
3.7%
l8908
 
3.1%
Other values (38)87356
30.4%
ValueCountFrequency (%)
-19087
89.1%
'1230
 
5.7%
1112
 
5.2%
/2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII299179
96.9%
None9467
 
3.1%

Most frequent character per block

ValueCountFrequency (%)
e50334
16.8%
r24149
 
8.1%
n21479
 
7.2%
a21477
 
7.2%
-19087
 
6.4%
i16864
 
5.6%
s16174
 
5.4%
t15581
 
5.2%
o14139
 
4.7%
u10754
 
3.6%
Other values (38)89141
29.8%
ValueCountFrequency (%)
é3398
35.9%
ô3364
35.5%
è2448
25.9%
ç257
 
2.7%

Code de la commune
Real number (ℝ≥0)

Distinct908
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean251.6796857
Minimum1
Maximum909
Zeros0
Zeros (%)0.0%
Memory size275.6 KiB
2021-02-18T22:26:16.883325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile21.75
Q1105
median217
Q3362
95-th percentile604
Maximum909
Range908
Interquartile range (IQR)257

Descriptive statistics

Standard deviation182.4299022
Coefficient of variation (CV)0.7248495313
Kurtosis0.1277895044
Mean251.6796857
Median Absolute Deviation (MAD)123
Skewness0.8208534191
Sum8873219
Variance33280.66923
MonotocityNot monotonic
2021-02-18T22:26:16.989865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7790
 
0.3%
6189
 
0.3%
4689
 
0.3%
10489
 
0.3%
1388
 
0.2%
5488
 
0.2%
7088
 
0.2%
188
 
0.2%
888
 
0.2%
6288
 
0.2%
Other values (898)34371
97.5%
ValueCountFrequency (%)
188
0.2%
286
0.2%
383
0.2%
483
0.2%
583
0.2%
ValueCountFrequency (%)
9091
< 0.1%
9081
< 0.1%
9071
< 0.1%
9061
< 0.1%
9051
< 0.1%

Libellé de la commune
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size275.6 KiB

Inscrits
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct5100
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1342.901293
Minimum5
Maximum1306831
Zeros0
Zeros (%)0.0%
Memory size275.6 KiB
2021-02-18T22:26:17.099381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile63
Q1162
median354
Q3882
95-th percentile4658.5
Maximum1306831
Range1306826
Interquartile range (IQR)720

Descriptive statistics

Standard deviation8948.038398
Coefficient of variation (CV)6.663213776
Kurtosis13189.53253
Mean1342.901293
Median Absolute Deviation (MAD)240
Skewness96.47532146
Sum47345328
Variance80067391.16
MonotocityNot monotonic
2021-02-18T22:26:17.217954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12390
 
0.3%
9890
 
0.3%
12190
 
0.3%
9586
 
0.2%
12086
 
0.2%
8386
 
0.2%
11484
 
0.2%
8484
 
0.2%
13883
 
0.2%
15283
 
0.2%
Other values (5090)34394
97.6%
ValueCountFrequency (%)
51
 
< 0.1%
82
< 0.1%
91
 
< 0.1%
102
< 0.1%
113
< 0.1%
ValueCountFrequency (%)
13068311
< 0.1%
4985701
< 0.1%
2691761
< 0.1%
2428231
< 0.1%
2142941
< 0.1%

Abstentions
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct3571
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean669.803381
Minimum0
Maximum550449
Zeros5
Zeros (%)< 0.1%
Memory size275.6 KiB
2021-02-18T22:26:17.511190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22
Q165
median152
Q3397
95-th percentile2316
Maximum550449
Range550449
Interquartile range (IQR)332

Descriptive statistics

Standard deviation4242.351022
Coefficient of variation (CV)6.3337259
Kurtosis8617.817814
Mean669.803381
Median Absolute Deviation (MAD)108
Skewness74.49258578
Sum23614588
Variance17997542.19
MonotocityNot monotonic
2021-02-18T22:26:17.612786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38189
 
0.5%
54186
 
0.5%
35184
 
0.5%
40184
 
0.5%
42181
 
0.5%
32181
 
0.5%
50181
 
0.5%
52181
 
0.5%
23180
 
0.5%
36175
 
0.5%
Other values (3561)33434
94.8%
ValueCountFrequency (%)
05
 
< 0.1%
111
< 0.1%
215
< 0.1%
317
< 0.1%
424
0.1%
ValueCountFrequency (%)
5504491
< 0.1%
2802331
< 0.1%
1156201
< 0.1%
1148921
< 0.1%
1118941
< 0.1%

% Abs/Ins
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3960
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.13435415
Minimum0
Maximum100
Zeros5
Zeros (%)< 0.1%
Memory size275.6 KiB
2021-02-18T22:26:17.718442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30.37
Q138.63
median43.23
Q347.48
95-th percentile54.28
Maximum100
Range100
Interquartile range (IQR)8.85

Descriptive statistics

Standard deviation8.22291111
Coefficient of variation (CV)0.1906348495
Kurtosis5.448081089
Mean43.13435415
Median Absolute Deviation (MAD)4.42
Skewness0.7710541674
Sum1520744.79
Variance67.61626713
MonotocityNot monotonic
2021-02-18T22:26:17.818483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.33198
 
0.6%
40161
 
0.5%
50159
 
0.5%
37.5123
 
0.3%
42.86121
 
0.3%
44.4485
 
0.2%
41.6775
 
0.2%
36.3673
 
0.2%
41.1867
 
0.2%
42.1166
 
0.2%
Other values (3950)34128
96.8%
ValueCountFrequency (%)
05
< 0.1%
0.121
 
< 0.1%
41
 
< 0.1%
4.551
 
< 0.1%
4.761
 
< 0.1%
ValueCountFrequency (%)
1001
< 0.1%
99.31
< 0.1%
96.741
< 0.1%
96.631
< 0.1%
96.011
< 0.1%

Votants
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct3536
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean673.0979124
Minimum0
Maximum756382
Zeros1
Zeros (%)< 0.1%
Memory size275.6 KiB
2021-02-18T22:26:17.917682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38
Q195
median200
Q3479
95-th percentile2290.5
Maximum756382
Range756382
Interquartile range (IQR)384

Descriptive statistics

Standard deviation4825.492984
Coefficient of variation (CV)7.1690803
Kurtosis17242.25547
Mean673.0979124
Median Absolute Deviation (MAD)131
Skewness114.5210326
Sum23730740
Variance23285382.54
MonotocityNot monotonic
2021-02-18T22:26:18.018401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70151
 
0.4%
67149
 
0.4%
53148
 
0.4%
77146
 
0.4%
61140
 
0.4%
93138
 
0.4%
54137
 
0.4%
84136
 
0.4%
66135
 
0.4%
68135
 
0.4%
Other values (3526)33841
96.0%
ValueCountFrequency (%)
01
 
< 0.1%
32
 
< 0.1%
41
 
< 0.1%
55
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
7563821
< 0.1%
2183371
< 0.1%
1542841
< 0.1%
1272031
< 0.1%
1024001
< 0.1%

% Vot/Ins
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3961
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.86568414
Minimum0
Maximum100
Zeros1
Zeros (%)< 0.1%
Memory size275.6 KiB
2021-02-18T22:26:18.127945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile45.72
Q152.52
median56.77
Q361.37
95-th percentile69.63
Maximum100
Range100
Interquartile range (IQR)8.85

Descriptive statistics

Standard deviation8.222924943
Coefficient of variation (CV)0.1446025853
Kurtosis5.448053164
Mean56.86568414
Median Absolute Deviation (MAD)4.42
Skewness-0.7710464923
Sum2004856.56
Variance67.61649462
MonotocityNot monotonic
2021-02-18T22:26:18.230533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.67198
 
0.6%
60161
 
0.5%
50159
 
0.5%
62.5123
 
0.3%
57.14121
 
0.3%
55.5685
 
0.2%
58.3375
 
0.2%
63.6473
 
0.2%
58.8267
 
0.2%
57.8966
 
0.2%
Other values (3951)34128
96.8%
ValueCountFrequency (%)
01
< 0.1%
0.71
< 0.1%
3.261
< 0.1%
3.371
< 0.1%
3.991
< 0.1%
ValueCountFrequency (%)
1005
< 0.1%
99.881
 
< 0.1%
961
 
< 0.1%
95.451
 
< 0.1%
95.241
 
< 0.1%

Blancs
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct361
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.74293737
Minimum0
Maximum6315
Zeros2730
Zeros (%)7.7%
Memory size275.6 KiB
2021-02-18T22:26:18.332659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q314
95-th percentile54
Maximum6315
Range6315
Interquartile range (IQR)11

Descriptive statistics

Standard deviation57.35866927
Coefficient of variation (CV)3.643454072
Kurtosis4546.676296
Mean15.74293737
Median Absolute Deviation (MAD)4
Skewness49.67641847
Sum555033
Variance3290.016941
MonotocityNot monotonic
2021-02-18T22:26:18.435898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23103
 
8.8%
12917
 
8.3%
32833
 
8.0%
02730
 
7.7%
42570
 
7.3%
52078
 
5.9%
61902
 
5.4%
71624
 
4.6%
81435
 
4.1%
91225
 
3.5%
Other values (351)12839
36.4%
ValueCountFrequency (%)
02730
7.7%
12917
8.3%
23103
8.8%
32833
8.0%
42570
7.3%
ValueCountFrequency (%)
63151
< 0.1%
31601
< 0.1%
20921
< 0.1%
18971
< 0.1%
14331
< 0.1%

% Blancs/Ins
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct807
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.821162923
Minimum0
Maximum35.29
Zeros2730
Zeros (%)7.7%
Memory size275.6 KiB
2021-02-18T22:26:18.546296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.96
median1.54
Q32.34
95-th percentile4.38
Maximum35.29
Range35.29
Interquartile range (IQR)1.38

Descriptive statistics

Standard deviation1.474025924
Coefficient of variation (CV)0.8093871809
Kurtosis27.07545748
Mean1.821162923
Median Absolute Deviation (MAD)0.67
Skewness3.148135004
Sum64206.92
Variance2.172752424
MonotocityNot monotonic
2021-02-18T22:26:18.651138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02730
 
7.7%
1.69202
 
0.6%
1.41189
 
0.5%
1.19185
 
0.5%
1.52184
 
0.5%
1.32178
 
0.5%
1.18176
 
0.5%
1.2175
 
0.5%
1.67172
 
0.5%
1.1171
 
0.5%
Other values (797)30894
87.6%
ValueCountFrequency (%)
02730
7.7%
0.012
 
< 0.1%
0.023
 
< 0.1%
0.0312
 
< 0.1%
0.0413
 
< 0.1%
ValueCountFrequency (%)
35.291
< 0.1%
252
< 0.1%
24.141
< 0.1%
23.081
< 0.1%
21.431
< 0.1%

% Blancs/Vot
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1094
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.171485421
Minimum0
Maximum42.86
Zeros2730
Zeros (%)7.7%
Memory size275.6 KiB
2021-02-18T22:26:18.764725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.76
median2.78
Q34.11
95-th percentile7.2925
Maximum42.86
Range42.86
Interquartile range (IQR)2.35

Descriptive statistics

Standard deviation2.365087146
Coefficient of variation (CV)0.7457348314
Kurtosis15.73741671
Mean3.171485421
Median Absolute Deviation (MAD)1.14
Skewness2.431141404
Sum111813.89
Variance5.593637206
MonotocityNot monotonic
2021-02-18T22:26:18.869589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02730
 
7.7%
2.33174
 
0.5%
3.13168
 
0.5%
3.7164
 
0.5%
2.22162
 
0.5%
2.94155
 
0.4%
2.86154
 
0.4%
2.56154
 
0.4%
2.44150
 
0.4%
3.33148
 
0.4%
Other values (1084)31097
88.2%
ValueCountFrequency (%)
02730
7.7%
0.051
 
< 0.1%
0.081
 
< 0.1%
0.091
 
< 0.1%
0.11
 
< 0.1%
ValueCountFrequency (%)
42.861
< 0.1%
401
< 0.1%
35.291
< 0.1%
33.331
< 0.1%
31.581
< 0.1%

Nuls
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct352
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.76438053
Minimum0
Maximum6010
Zeros3067
Zeros (%)8.7%
Memory size275.6 KiB
2021-02-18T22:26:18.976807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q313
95-th percentile52
Maximum6010
Range6010
Interquartile range (IQR)11

Descriptive statistics

Standard deviation51.85282887
Coefficient of variation (CV)3.512021975
Kurtosis5303.236806
Mean14.76438053
Median Absolute Deviation (MAD)4
Skewness52.51286955
Sum520533
Variance2688.715862
MonotocityNot monotonic
2021-02-18T22:26:19.079734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13177
 
9.0%
23159
 
9.0%
03067
 
8.7%
32847
 
8.1%
42568
 
7.3%
52218
 
6.3%
61881
 
5.3%
71700
 
4.8%
81426
 
4.0%
91128
 
3.2%
Other values (342)12085
34.3%
ValueCountFrequency (%)
03067
8.7%
13177
9.0%
23159
9.0%
32847
8.1%
42568
7.3%
ValueCountFrequency (%)
60101
< 0.1%
23931
< 0.1%
16721
< 0.1%
14561
< 0.1%
13081
< 0.1%

% Nuls/Ins
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct769
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.681791752
Minimum0
Maximum44.09
Zeros3067
Zeros (%)8.7%
Memory size275.6 KiB
2021-02-18T22:26:19.191457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.82
median1.41
Q32.25
95-th percentile4.14
Maximum44.09
Range44.09
Interquartile range (IQR)1.43

Descriptive statistics

Standard deviation1.341532655
Coefficient of variation (CV)0.7976806011
Kurtosis38.37323701
Mean1.681791752
Median Absolute Deviation (MAD)0.69
Skewness2.755017793
Sum59293.25
Variance1.799709866
MonotocityNot monotonic
2021-02-18T22:26:19.294780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03067
 
8.7%
1.2185
 
0.5%
0.9181
 
0.5%
1.52179
 
0.5%
1.08177
 
0.5%
1.1176
 
0.5%
0.85176
 
0.5%
1.16174
 
0.5%
1.23172
 
0.5%
0.95171
 
0.5%
Other values (759)30598
86.8%
ValueCountFrequency (%)
03067
8.7%
0.021
 
< 0.1%
0.036
 
< 0.1%
0.041
 
< 0.1%
0.053
 
< 0.1%
ValueCountFrequency (%)
44.091
< 0.1%
29.141
< 0.1%
14.712
< 0.1%
13.561
< 0.1%
13.511
< 0.1%

% Nuls/Vot
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1097
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.944822442
Minimum0
Maximum44.15
Zeros3067
Zeros (%)8.7%
Memory size275.6 KiB
2021-02-18T22:26:19.402849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.49
median2.54
Q33.95
95-th percentile7.02
Maximum44.15
Range44.15
Interquartile range (IQR)2.46

Descriptive statistics

Standard deviation2.216483632
Coefficient of variation (CV)0.7526714007
Kurtosis12.06057987
Mean2.944822442
Median Absolute Deviation (MAD)1.18
Skewness1.932092722
Sum103822.66
Variance4.91279969
MonotocityNot monotonic
2021-02-18T22:26:19.738849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03067
 
8.7%
2.44164
 
0.5%
3.45162
 
0.5%
3.33156
 
0.4%
3.23155
 
0.4%
2.86147
 
0.4%
2.78146
 
0.4%
3.13145
 
0.4%
2.17145
 
0.4%
3.7144
 
0.4%
Other values (1087)30825
87.4%
ValueCountFrequency (%)
03067
8.7%
0.051
 
< 0.1%
0.061
 
< 0.1%
0.111
 
< 0.1%
0.121
 
< 0.1%
ValueCountFrequency (%)
44.151
< 0.1%
38.61
< 0.1%
381
< 0.1%
28.631
< 0.1%
28.261
< 0.1%

Exprimés
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct3452
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean642.5905945
Minimum0
Maximum744057
Zeros1
Zeros (%)< 0.1%
Memory size275.6 KiB
2021-02-18T22:26:19.844208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q189
median187
Q3451
95-th percentile2184.25
Maximum744057
Range744057
Interquartile range (IQR)362

Descriptive statistics

Standard deviation4729.810887
Coefficient of variation (CV)7.360535506
Kurtosis17488.88353
Mean642.5905945
Median Absolute Deviation (MAD)123
Skewness115.647076
Sum22655174
Variance22371111.02
MonotocityNot monotonic
2021-02-18T22:26:19.944251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68154
 
0.4%
52154
 
0.4%
55153
 
0.4%
69150
 
0.4%
63150
 
0.4%
60149
 
0.4%
82149
 
0.4%
71147
 
0.4%
56147
 
0.4%
66146
 
0.4%
Other values (3442)33757
95.7%
ValueCountFrequency (%)
01
 
< 0.1%
32
 
< 0.1%
41
 
< 0.1%
57
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
7440571
< 0.1%
2127841
< 0.1%
1517271
< 0.1%
1247151
< 0.1%
994171
< 0.1%

% Exp/Ins
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3809
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.36270025
Minimum0
Maximum100
Zeros1
Zeros (%)< 0.1%
Memory size275.6 KiB
2021-02-18T22:26:20.052850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile42.74
Q149.25
median53.3
Q357.62
95-th percentile65.41
Maximum100
Range100
Interquartile range (IQR)8.37

Descriptive statistics

Standard deviation7.781436068
Coefficient of variation (CV)0.1458216326
Kurtosis5.352783208
Mean53.36270025
Median Absolute Deviation (MAD)4.17
Skewness-0.731753401
Sum1881355.36
Variance60.55074728
MonotocityNot monotonic
2021-02-18T22:26:20.152177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50323
 
0.9%
60129
 
0.4%
57.14109
 
0.3%
55.56108
 
0.3%
66.6786
 
0.2%
53.8584
 
0.2%
62.578
 
0.2%
54.5578
 
0.2%
58.3375
 
0.2%
56.2563
 
0.2%
Other values (3799)34123
96.8%
ValueCountFrequency (%)
01
< 0.1%
0.71
< 0.1%
3.111
< 0.1%
3.271
< 0.1%
3.541
< 0.1%
ValueCountFrequency (%)
1003
< 0.1%
951
 
< 0.1%
92.311
 
< 0.1%
91.891
 
< 0.1%
91.181
 
< 0.1%

% Exp/Vot
Real number (ℝ≥0)

Distinct1619
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.88095643
Minimum0
Maximum100
Zeros1
Zeros (%)< 0.1%
Memory size275.6 KiB
2021-02-18T22:26:20.252619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile87.88
Q192.26
median94.42
Q396.1
95-th percentile98.15
Maximum100
Range100
Interquartile range (IQR)3.84

Descriptive statistics

Standard deviation3.387010627
Coefficient of variation (CV)0.03607771752
Kurtosis22.55443682
Mean93.88095643
Median Absolute Deviation (MAD)1.88
Skewness-2.059590841
Sum3309867
Variance11.47184099
MonotocityNot monotonic
2021-02-18T22:26:20.353488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100774
 
2.2%
92.86207
 
0.6%
94.44186
 
0.5%
93.33186
 
0.5%
91.67185
 
0.5%
94.12182
 
0.5%
93.75178
 
0.5%
95.24175
 
0.5%
92.31165
 
0.5%
95161
 
0.5%
Other values (1609)32857
93.2%
ValueCountFrequency (%)
01
< 0.1%
54.761
< 0.1%
56.141
< 0.1%
57.141
< 0.1%
601
< 0.1%
ValueCountFrequency (%)
100774
2.2%
99.741
 
< 0.1%
99.552
 
< 0.1%
99.461
 
< 0.1%
99.451
 
< 0.1%

N°Liste
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
1
35256 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters35256
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
135256
100.0%
2021-02-18T22:26:20.514082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:20.562772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
135256
100.0%

Most occurring characters

ValueCountFrequency (%)
135256
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number35256
100.0%

Most frequent character per category

ValueCountFrequency (%)
135256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common35256
100.0%

Most frequent character per script

ValueCountFrequency (%)
135256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII35256
100.0%

Most frequent character per block

ValueCountFrequency (%)
135256
100.0%

Libellé Abrégé Liste
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
LA FRANCE INSOUMISE
35256 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters669864
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLA FRANCE INSOUMISE
2nd rowLA FRANCE INSOUMISE
3rd rowLA FRANCE INSOUMISE
4th rowLA FRANCE INSOUMISE
5th rowLA FRANCE INSOUMISE
ValueCountFrequency (%)
LA FRANCE INSOUMISE35256
100.0%
2021-02-18T22:26:20.682557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:20.732878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
insoumise35256
33.3%
la35256
33.3%
france35256
33.3%

Most occurring characters

ValueCountFrequency (%)
A70512
10.5%
70512
10.5%
N70512
10.5%
E70512
10.5%
I70512
10.5%
S70512
10.5%
L35256
 
5.3%
F35256
 
5.3%
R35256
 
5.3%
C35256
 
5.3%
Other values (3)105768
15.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter599352
89.5%
Space Separator70512
 
10.5%

Most frequent character per category

ValueCountFrequency (%)
A70512
11.8%
N70512
11.8%
E70512
11.8%
I70512
11.8%
S70512
11.8%
L35256
5.9%
F35256
5.9%
R35256
5.9%
C35256
5.9%
O35256
5.9%
Other values (2)70512
11.8%
ValueCountFrequency (%)
70512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin599352
89.5%
Common70512
 
10.5%

Most frequent character per script

ValueCountFrequency (%)
A70512
11.8%
N70512
11.8%
E70512
11.8%
I70512
11.8%
S70512
11.8%
L35256
5.9%
F35256
5.9%
R35256
5.9%
C35256
5.9%
O35256
5.9%
Other values (2)70512
11.8%
ValueCountFrequency (%)
70512
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII669864
100.0%

Most frequent character per block

ValueCountFrequency (%)
A70512
10.5%
70512
10.5%
N70512
10.5%
E70512
10.5%
I70512
10.5%
S70512
10.5%
L35256
 
5.3%
F35256
 
5.3%
R35256
 
5.3%
C35256
 
5.3%
Other values (3)105768
15.8%

Libellé Etendu Liste
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
LA FRANCE INSOUMISE
35256 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters669864
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLA FRANCE INSOUMISE
2nd rowLA FRANCE INSOUMISE
3rd rowLA FRANCE INSOUMISE
4th rowLA FRANCE INSOUMISE
5th rowLA FRANCE INSOUMISE
ValueCountFrequency (%)
LA FRANCE INSOUMISE35256
100.0%
2021-02-18T22:26:20.857693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:20.909610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
insoumise35256
33.3%
la35256
33.3%
france35256
33.3%

Most occurring characters

ValueCountFrequency (%)
A70512
10.5%
70512
10.5%
N70512
10.5%
E70512
10.5%
I70512
10.5%
S70512
10.5%
L35256
 
5.3%
F35256
 
5.3%
R35256
 
5.3%
C35256
 
5.3%
Other values (3)105768
15.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter599352
89.5%
Space Separator70512
 
10.5%

Most frequent character per category

ValueCountFrequency (%)
A70512
11.8%
N70512
11.8%
E70512
11.8%
I70512
11.8%
S70512
11.8%
L35256
5.9%
F35256
5.9%
R35256
5.9%
C35256
5.9%
O35256
5.9%
Other values (2)70512
11.8%
ValueCountFrequency (%)
70512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin599352
89.5%
Common70512
 
10.5%

Most frequent character per script

ValueCountFrequency (%)
A70512
11.8%
N70512
11.8%
E70512
11.8%
I70512
11.8%
S70512
11.8%
L35256
5.9%
F35256
5.9%
R35256
5.9%
C35256
5.9%
O35256
5.9%
Other values (2)70512
11.8%
ValueCountFrequency (%)
70512
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII669864
100.0%

Most frequent character per block

ValueCountFrequency (%)
A70512
10.5%
70512
10.5%
N70512
10.5%
E70512
10.5%
I70512
10.5%
S70512
10.5%
L35256
 
5.3%
F35256
 
5.3%
R35256
 
5.3%
C35256
 
5.3%
Other values (3)105768
15.8%

Nom Tête de Liste
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
AUBRY Manon
35256 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters387816
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAUBRY Manon
2nd rowAUBRY Manon
3rd rowAUBRY Manon
4th rowAUBRY Manon
5th rowAUBRY Manon
ValueCountFrequency (%)
AUBRY Manon35256
100.0%
2021-02-18T22:26:21.036483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:21.089268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
manon35256
50.0%
aubry35256
50.0%

Most occurring characters

ValueCountFrequency (%)
n70512
18.2%
A35256
9.1%
U35256
9.1%
B35256
9.1%
R35256
9.1%
Y35256
9.1%
35256
9.1%
M35256
9.1%
a35256
9.1%
o35256
9.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter211536
54.5%
Lowercase Letter141024
36.4%
Space Separator35256
 
9.1%

Most frequent character per category

ValueCountFrequency (%)
A35256
16.7%
U35256
16.7%
B35256
16.7%
R35256
16.7%
Y35256
16.7%
M35256
16.7%
ValueCountFrequency (%)
n70512
50.0%
a35256
25.0%
o35256
25.0%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin352560
90.9%
Common35256
 
9.1%

Most frequent character per script

ValueCountFrequency (%)
n70512
20.0%
A35256
10.0%
U35256
10.0%
B35256
10.0%
R35256
10.0%
Y35256
10.0%
M35256
10.0%
a35256
10.0%
o35256
10.0%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII387816
100.0%

Most frequent character per block

ValueCountFrequency (%)
n70512
18.2%
A35256
9.1%
U35256
9.1%
B35256
9.1%
R35256
9.1%
Y35256
9.1%
35256
9.1%
M35256
9.1%
a35256
9.1%
o35256
9.1%

Voix
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct740
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.5192875
Minimum0
Maximum39515
Zeros1406
Zeros (%)4.0%
Memory size275.6 KiB
2021-02-18T22:26:21.157034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median11
Q328
95-th percentile132
Maximum39515
Range39515
Interquartile range (IQR)23

Descriptive statistics

Standard deviation284.0306803
Coefficient of variation (CV)7.009764925
Kurtosis11114.78167
Mean40.5192875
Median Absolute Deviation (MAD)8
Skewness87.84448745
Sum1428548
Variance80673.42734
MonotocityNot monotonic
2021-02-18T22:26:21.288598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31882
 
5.3%
41820
 
5.2%
21807
 
5.1%
51703
 
4.8%
11691
 
4.8%
61544
 
4.4%
01406
 
4.0%
71396
 
4.0%
81279
 
3.6%
91255
 
3.6%
Other values (730)19473
55.2%
ValueCountFrequency (%)
01406
4.0%
11691
4.8%
21807
5.1%
31882
5.3%
41820
5.2%
ValueCountFrequency (%)
395151
< 0.1%
175211
< 0.1%
109421
< 0.1%
89041
< 0.1%
67161
< 0.1%

% Voix/Ins
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1157
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.264884559
Minimum0
Maximum25
Zeros1406
Zeros (%)4.0%
Memory size275.6 KiB
2021-02-18T22:26:21.415148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.58
Q12
median2.96
Q34.13
95-th percentile6.9
Maximum25
Range25
Interquartile range (IQR)2.13

Descriptive statistics

Standard deviation2.043899617
Coefficient of variation (CV)0.6260250799
Kurtosis8.207719298
Mean3.264884559
Median Absolute Deviation (MAD)1.05
Skewness1.82338562
Sum115106.77
Variance4.177525643
MonotocityNot monotonic
2021-02-18T22:26:21.534682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01406
 
4.0%
2.86142
 
0.4%
2.63140
 
0.4%
2.5133
 
0.4%
3.03133
 
0.4%
3.45130
 
0.4%
2.22129
 
0.4%
3.13129
 
0.4%
3.23127
 
0.4%
2.38125
 
0.4%
Other values (1147)32662
92.6%
ValueCountFrequency (%)
01406
4.0%
0.021
 
< 0.1%
0.031
 
< 0.1%
0.041
 
< 0.1%
0.161
 
< 0.1%
ValueCountFrequency (%)
254
< 0.1%
23.811
 
< 0.1%
23.641
 
< 0.1%
22.51
 
< 0.1%
22.223
< 0.1%

% Voix/Exp
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1736
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.132509644
Minimum0
Maximum72.11
Zeros1406
Zeros (%)4.0%
Memory size275.6 KiB
2021-02-18T22:26:21.648789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.15
Q13.85
median5.69
Q37.8
95-th percentile12.5
Maximum72.11
Range72.11
Interquartile range (IQR)3.95

Descriptive statistics

Standard deviation3.617781267
Coefficient of variation (CV)0.5899348679
Kurtosis8.483320696
Mean6.132509644
Median Absolute Deviation (MAD)1.97
Skewness1.594035628
Sum216207.76
Variance13.08834129
MonotocityNot monotonic
2021-02-18T22:26:21.780804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01406
 
4.0%
6.67190
 
0.5%
5.88186
 
0.5%
6.25178
 
0.5%
7.69173
 
0.5%
7.14168
 
0.5%
5167
 
0.5%
5.26162
 
0.5%
5.56156
 
0.4%
8.33154
 
0.4%
Other values (1726)32316
91.7%
ValueCountFrequency (%)
01406
4.0%
0.381
 
< 0.1%
0.391
 
< 0.1%
0.431
 
< 0.1%
0.451
 
< 0.1%
ValueCountFrequency (%)
72.111
 
< 0.1%
41.381
 
< 0.1%
40.911
 
< 0.1%
403
< 0.1%
35.481
 
< 0.1%

Unnamed: 25
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
2
35256 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters35256
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2
ValueCountFrequency (%)
235256
100.0%
2021-02-18T22:26:22.014395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:22.068633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
235256
100.0%

Most occurring characters

ValueCountFrequency (%)
235256
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number35256
100.0%

Most frequent character per category

ValueCountFrequency (%)
235256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common35256
100.0%

Most frequent character per script

ValueCountFrequency (%)
235256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII35256
100.0%

Most frequent character per block

ValueCountFrequency (%)
235256
100.0%

Unnamed: 26
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
UNE FRANCE ROYALE
35256 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters599352
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUNE FRANCE ROYALE
2nd rowUNE FRANCE ROYALE
3rd rowUNE FRANCE ROYALE
4th rowUNE FRANCE ROYALE
5th rowUNE FRANCE ROYALE
ValueCountFrequency (%)
UNE FRANCE ROYALE35256
100.0%
2021-02-18T22:26:22.200517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:22.255047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
une35256
33.3%
royale35256
33.3%
france35256
33.3%

Most occurring characters

ValueCountFrequency (%)
E105768
17.6%
N70512
11.8%
70512
11.8%
R70512
11.8%
A70512
11.8%
U35256
 
5.9%
F35256
 
5.9%
C35256
 
5.9%
O35256
 
5.9%
Y35256
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter528840
88.2%
Space Separator70512
 
11.8%

Most frequent character per category

ValueCountFrequency (%)
E105768
20.0%
N70512
13.3%
R70512
13.3%
A70512
13.3%
U35256
 
6.7%
F35256
 
6.7%
C35256
 
6.7%
O35256
 
6.7%
Y35256
 
6.7%
L35256
 
6.7%
ValueCountFrequency (%)
70512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin528840
88.2%
Common70512
 
11.8%

Most frequent character per script

ValueCountFrequency (%)
E105768
20.0%
N70512
13.3%
R70512
13.3%
A70512
13.3%
U35256
 
6.7%
F35256
 
6.7%
C35256
 
6.7%
O35256
 
6.7%
Y35256
 
6.7%
L35256
 
6.7%
ValueCountFrequency (%)
70512
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII599352
100.0%

Most frequent character per block

ValueCountFrequency (%)
E105768
17.6%
N70512
11.8%
70512
11.8%
R70512
11.8%
A70512
11.8%
U35256
 
5.9%
F35256
 
5.9%
C35256
 
5.9%
O35256
 
5.9%
Y35256
 
5.9%

Unnamed: 27
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
UNE FRANCE ROYALE AU COEUR DE L'EUROPE
35256 

Length

Max length38
Median length38
Mean length38
Min length38

Characters and Unicode

Total characters1339728
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUNE FRANCE ROYALE AU COEUR DE L'EUROPE
2nd rowUNE FRANCE ROYALE AU COEUR DE L'EUROPE
3rd rowUNE FRANCE ROYALE AU COEUR DE L'EUROPE
4th rowUNE FRANCE ROYALE AU COEUR DE L'EUROPE
5th rowUNE FRANCE ROYALE AU COEUR DE L'EUROPE
ValueCountFrequency (%)
UNE FRANCE ROYALE AU COEUR DE L'EUROPE35256
100.0%
2021-02-18T22:26:22.398574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:22.458042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
de35256
14.3%
coeur35256
14.3%
au35256
14.3%
royale35256
14.3%
france35256
14.3%
une35256
14.3%
l'europe35256
14.3%

Most occurring characters

ValueCountFrequency (%)
E246792
18.4%
211536
15.8%
U141024
10.5%
R141024
10.5%
A105768
7.9%
O105768
7.9%
N70512
 
5.3%
C70512
 
5.3%
L70512
 
5.3%
F35256
 
2.6%
Other values (4)141024
10.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1092936
81.6%
Space Separator211536
 
15.8%
Other Punctuation35256
 
2.6%

Most frequent character per category

ValueCountFrequency (%)
E246792
22.6%
U141024
12.9%
R141024
12.9%
A105768
9.7%
O105768
9.7%
N70512
 
6.5%
C70512
 
6.5%
L70512
 
6.5%
F35256
 
3.2%
Y35256
 
3.2%
Other values (2)70512
 
6.5%
ValueCountFrequency (%)
211536
100.0%
ValueCountFrequency (%)
'35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1092936
81.6%
Common246792
 
18.4%

Most frequent character per script

ValueCountFrequency (%)
E246792
22.6%
U141024
12.9%
R141024
12.9%
A105768
9.7%
O105768
9.7%
N70512
 
6.5%
C70512
 
6.5%
L70512
 
6.5%
F35256
 
3.2%
Y35256
 
3.2%
Other values (2)70512
 
6.5%
ValueCountFrequency (%)
211536
85.7%
'35256
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1339728
100.0%

Most frequent character per block

ValueCountFrequency (%)
E246792
18.4%
211536
15.8%
U141024
10.5%
R141024
10.5%
A105768
7.9%
O105768
7.9%
N70512
 
5.3%
C70512
 
5.3%
L70512
 
5.3%
F35256
 
2.6%
Other values (4)141024
10.5%

Unnamed: 28
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
DE PREVOISIN Robert
35256 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters669864
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDE PREVOISIN Robert
2nd rowDE PREVOISIN Robert
3rd rowDE PREVOISIN Robert
4th rowDE PREVOISIN Robert
5th rowDE PREVOISIN Robert
ValueCountFrequency (%)
DE PREVOISIN Robert35256
100.0%
2021-02-18T22:26:22.612885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:22.685305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
de35256
33.3%
robert35256
33.3%
prevoisin35256
33.3%

Most occurring characters

ValueCountFrequency (%)
E70512
 
10.5%
70512
 
10.5%
R70512
 
10.5%
I70512
 
10.5%
D35256
 
5.3%
P35256
 
5.3%
V35256
 
5.3%
O35256
 
5.3%
S35256
 
5.3%
N35256
 
5.3%
Other values (5)176280
26.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter423072
63.2%
Lowercase Letter176280
26.3%
Space Separator70512
 
10.5%

Most frequent character per category

ValueCountFrequency (%)
E70512
16.7%
R70512
16.7%
I70512
16.7%
D35256
8.3%
P35256
8.3%
V35256
8.3%
O35256
8.3%
S35256
8.3%
N35256
8.3%
ValueCountFrequency (%)
o35256
20.0%
b35256
20.0%
e35256
20.0%
r35256
20.0%
t35256
20.0%
ValueCountFrequency (%)
70512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin599352
89.5%
Common70512
 
10.5%

Most frequent character per script

ValueCountFrequency (%)
E70512
11.8%
R70512
11.8%
I70512
11.8%
D35256
 
5.9%
P35256
 
5.9%
V35256
 
5.9%
O35256
 
5.9%
S35256
 
5.9%
N35256
 
5.9%
o35256
 
5.9%
Other values (4)141024
23.5%
ValueCountFrequency (%)
70512
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII669864
100.0%

Most frequent character per block

ValueCountFrequency (%)
E70512
 
10.5%
70512
 
10.5%
R70512
 
10.5%
I70512
 
10.5%
D35256
 
5.3%
P35256
 
5.3%
V35256
 
5.3%
O35256
 
5.3%
S35256
 
5.3%
N35256
 
5.3%
Other values (5)176280
26.3%

Unnamed: 29
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08934649421
Minimum0
Maximum87
Zeros33892
Zeros (%)96.1%
Memory size275.6 KiB
2021-02-18T22:26:22.753643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum87
Range87
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.134027261
Coefficient of variation (CV)12.69246511
Kurtosis2782.88824
Mean0.08934649421
Median Absolute Deviation (MAD)0
Skewness45.40257569
Sum3150
Variance1.286017828
MonotocityNot monotonic
2021-02-18T22:26:23.124523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
033892
96.1%
1916
 
2.6%
2234
 
0.7%
370
 
0.2%
443
 
0.1%
522
 
0.1%
713
 
< 0.1%
612
 
< 0.1%
88
 
< 0.1%
97
 
< 0.1%
Other values (23)39
 
0.1%
ValueCountFrequency (%)
033892
96.1%
1916
 
2.6%
2234
 
0.7%
370
 
0.2%
443
 
0.1%
ValueCountFrequency (%)
871
< 0.1%
801
< 0.1%
781
< 0.1%
581
< 0.1%
491
< 0.1%

Unnamed: 30
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct117
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.006719990924
Minimum0
Maximum9.52
Zeros33978
Zeros (%)96.4%
Memory size275.6 KiB
2021-02-18T22:26:23.224285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9.52
Range9.52
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.08753257387
Coefficient of variation (CV)13.02569823
Kurtosis4293.800726
Mean0.006719990924
Median Absolute Deviation (MAD)0
Skewness48.91701411
Sum236.92
Variance0.007661951489
MonotocityNot monotonic
2021-02-18T22:26:23.324454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033978
96.4%
0.01228
 
0.6%
0.02153
 
0.4%
0.03102
 
0.3%
0.0474
 
0.2%
0.0552
 
0.1%
0.0750
 
0.1%
0.0847
 
0.1%
0.0645
 
0.1%
0.0936
 
0.1%
Other values (107)491
 
1.4%
ValueCountFrequency (%)
033978
96.4%
0.01228
 
0.6%
0.02153
 
0.4%
0.03102
 
0.3%
0.0474
 
0.2%
ValueCountFrequency (%)
9.521
< 0.1%
3.771
< 0.1%
3.61
< 0.1%
2.561
< 0.1%
2.441
< 0.1%

Unnamed: 31
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct171
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01241008623
Minimum0
Maximum12.5
Zeros33901
Zeros (%)96.2%
Memory size275.6 KiB
2021-02-18T22:26:23.422441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum12.5
Range12.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1434714685
Coefficient of variation (CV)11.56087604
Kurtosis2011.913882
Mean0.01241008623
Median Absolute Deviation (MAD)0
Skewness33.38518794
Sum437.53
Variance0.02058406226
MonotocityNot monotonic
2021-02-18T22:26:23.529188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033901
96.2%
0.01131
 
0.4%
0.02119
 
0.3%
0.0390
 
0.3%
0.0471
 
0.2%
0.0568
 
0.2%
0.0648
 
0.1%
0.0743
 
0.1%
0.0836
 
0.1%
0.0936
 
0.1%
Other values (161)713
 
2.0%
ValueCountFrequency (%)
033901
96.2%
0.01131
 
0.4%
0.02119
 
0.3%
0.0390
 
0.3%
0.0471
 
0.2%
ValueCountFrequency (%)
12.51
< 0.1%
6.351
< 0.1%
5.261
< 0.1%
4.741
< 0.1%
4.351
< 0.1%

Unnamed: 32
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
3
35256 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters35256
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3
ValueCountFrequency (%)
335256
100.0%
2021-02-18T22:26:23.694218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:23.742676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
335256
100.0%

Most occurring characters

ValueCountFrequency (%)
335256
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number35256
100.0%

Most frequent character per category

ValueCountFrequency (%)
335256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common35256
100.0%

Most frequent character per script

ValueCountFrequency (%)
335256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII35256
100.0%

Most frequent character per block

ValueCountFrequency (%)
335256
100.0%

Unnamed: 33
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
LA LIGNE CLAIRE
35256 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters528840
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLA LIGNE CLAIRE
2nd rowLA LIGNE CLAIRE
3rd rowLA LIGNE CLAIRE
4th rowLA LIGNE CLAIRE
5th rowLA LIGNE CLAIRE
ValueCountFrequency (%)
LA LIGNE CLAIRE35256
100.0%
2021-02-18T22:26:23.861444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:23.910315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
claire35256
33.3%
ligne35256
33.3%
la35256
33.3%

Most occurring characters

ValueCountFrequency (%)
L105768
20.0%
A70512
13.3%
70512
13.3%
I70512
13.3%
E70512
13.3%
G35256
 
6.7%
N35256
 
6.7%
C35256
 
6.7%
R35256
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter458328
86.7%
Space Separator70512
 
13.3%

Most frequent character per category

ValueCountFrequency (%)
L105768
23.1%
A70512
15.4%
I70512
15.4%
E70512
15.4%
G35256
 
7.7%
N35256
 
7.7%
C35256
 
7.7%
R35256
 
7.7%
ValueCountFrequency (%)
70512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin458328
86.7%
Common70512
 
13.3%

Most frequent character per script

ValueCountFrequency (%)
L105768
23.1%
A70512
15.4%
I70512
15.4%
E70512
15.4%
G35256
 
7.7%
N35256
 
7.7%
C35256
 
7.7%
R35256
 
7.7%
ValueCountFrequency (%)
70512
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII528840
100.0%

Most frequent character per block

ValueCountFrequency (%)
L105768
20.0%
A70512
13.3%
70512
13.3%
I70512
13.3%
E70512
13.3%
G35256
 
6.7%
N35256
 
6.7%
C35256
 
6.7%
R35256
 
6.7%

Unnamed: 34
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
LA LIGNE CLAIRE
35256 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters528840
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLA LIGNE CLAIRE
2nd rowLA LIGNE CLAIRE
3rd rowLA LIGNE CLAIRE
4th rowLA LIGNE CLAIRE
5th rowLA LIGNE CLAIRE
ValueCountFrequency (%)
LA LIGNE CLAIRE35256
100.0%
2021-02-18T22:26:24.030239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:24.079349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
claire35256
33.3%
ligne35256
33.3%
la35256
33.3%

Most occurring characters

ValueCountFrequency (%)
L105768
20.0%
A70512
13.3%
70512
13.3%
I70512
13.3%
E70512
13.3%
G35256
 
6.7%
N35256
 
6.7%
C35256
 
6.7%
R35256
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter458328
86.7%
Space Separator70512
 
13.3%

Most frequent character per category

ValueCountFrequency (%)
L105768
23.1%
A70512
15.4%
I70512
15.4%
E70512
15.4%
G35256
 
7.7%
N35256
 
7.7%
C35256
 
7.7%
R35256
 
7.7%
ValueCountFrequency (%)
70512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin458328
86.7%
Common70512
 
13.3%

Most frequent character per script

ValueCountFrequency (%)
L105768
23.1%
A70512
15.4%
I70512
15.4%
E70512
15.4%
G35256
 
7.7%
N35256
 
7.7%
C35256
 
7.7%
R35256
 
7.7%
ValueCountFrequency (%)
70512
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII528840
100.0%

Most frequent character per block

ValueCountFrequency (%)
L105768
20.0%
A70512
13.3%
70512
13.3%
I70512
13.3%
E70512
13.3%
G35256
 
6.7%
N35256
 
6.7%
C35256
 
6.7%
R35256
 
6.7%

Unnamed: 35
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
CAMUS Renaud
35256 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters423072
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCAMUS Renaud
2nd rowCAMUS Renaud
3rd rowCAMUS Renaud
4th rowCAMUS Renaud
5th rowCAMUS Renaud
ValueCountFrequency (%)
CAMUS Renaud35256
100.0%
2021-02-18T22:26:24.202151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:24.253625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
camus35256
50.0%
renaud35256
50.0%

Most occurring characters

ValueCountFrequency (%)
C35256
8.3%
A35256
8.3%
M35256
8.3%
U35256
8.3%
S35256
8.3%
35256
8.3%
R35256
8.3%
e35256
8.3%
n35256
8.3%
a35256
8.3%
Other values (2)70512
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter211536
50.0%
Lowercase Letter176280
41.7%
Space Separator35256
 
8.3%

Most frequent character per category

ValueCountFrequency (%)
C35256
16.7%
A35256
16.7%
M35256
16.7%
U35256
16.7%
S35256
16.7%
R35256
16.7%
ValueCountFrequency (%)
e35256
20.0%
n35256
20.0%
a35256
20.0%
u35256
20.0%
d35256
20.0%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin387816
91.7%
Common35256
 
8.3%

Most frequent character per script

ValueCountFrequency (%)
C35256
9.1%
A35256
9.1%
M35256
9.1%
U35256
9.1%
S35256
9.1%
R35256
9.1%
e35256
9.1%
n35256
9.1%
a35256
9.1%
u35256
9.1%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII423072
100.0%

Most frequent character per block

ValueCountFrequency (%)
C35256
8.3%
A35256
8.3%
M35256
8.3%
U35256
8.3%
S35256
8.3%
35256
8.3%
R35256
8.3%
e35256
8.3%
n35256
8.3%
a35256
8.3%
Other values (2)70512
16.7%

Unnamed: 36
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04475833901
Minimum0
Maximum72
Zeros34839
Zeros (%)98.8%
Memory size275.6 KiB
2021-02-18T22:26:24.307068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum72
Range72
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9320917887
Coefficient of variation (CV)20.82498612
Kurtosis2271.337232
Mean0.04475833901
Median Absolute Deviation (MAD)0
Skewness42.23300978
Sum1578
Variance0.8687951026
MonotocityNot monotonic
2021-02-18T22:26:24.398094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
034839
98.8%
1249
 
0.7%
265
 
0.2%
322
 
0.1%
411
 
< 0.1%
510
 
< 0.1%
77
 
< 0.1%
66
 
< 0.1%
86
 
< 0.1%
115
 
< 0.1%
Other values (23)36
 
0.1%
ValueCountFrequency (%)
034839
98.8%
1249
 
0.7%
265
 
0.2%
322
 
0.1%
411
 
< 0.1%
ValueCountFrequency (%)
721
< 0.1%
591
< 0.1%
451
< 0.1%
431
< 0.1%
411
< 0.1%

Unnamed: 37
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct64
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001535341502
Minimum0
Maximum3.85
Zeros34886
Zeros (%)99.0%
Memory size275.6 KiB
2021-02-18T22:26:24.504778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3.85
Range3.85
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0419077713
Coefficient of variation (CV)27.29540707
Kurtosis4886.185095
Mean0.001535341502
Median Absolute Deviation (MAD)0
Skewness62.77599725
Sum54.13
Variance0.001756261295
MonotocityNot monotonic
2021-02-18T22:26:24.615769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034886
99.0%
0.01102
 
0.3%
0.0235
 
0.1%
0.0433
 
0.1%
0.0331
 
0.1%
0.0619
 
0.1%
0.0516
 
< 0.1%
0.0714
 
< 0.1%
0.19
 
< 0.1%
0.098
 
< 0.1%
Other values (54)103
 
0.3%
ValueCountFrequency (%)
034886
99.0%
0.01102
 
0.3%
0.0235
 
0.1%
0.0331
 
0.1%
0.0433
 
0.1%
ValueCountFrequency (%)
3.851
< 0.1%
3.681
< 0.1%
2.811
< 0.1%
2.671
< 0.1%
1.721
< 0.1%

Unnamed: 38
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct84
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.002850294985
Minimum0
Maximum6.67
Zeros34859
Zeros (%)98.9%
Memory size275.6 KiB
2021-02-18T22:26:24.733860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6.67
Range6.67
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.07401971738
Coefficient of variation (CV)25.96914276
Kurtosis4528.266373
Mean0.002850294985
Median Absolute Deviation (MAD)0
Skewness60.37475468
Sum100.49
Variance0.005478918561
MonotocityNot monotonic
2021-02-18T22:26:24.848514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034859
98.9%
0.0154
 
0.2%
0.0251
 
0.1%
0.0331
 
0.1%
0.0818
 
0.1%
0.0417
 
< 0.1%
0.0617
 
< 0.1%
0.0517
 
< 0.1%
0.0914
 
< 0.1%
0.0710
 
< 0.1%
Other values (74)168
 
0.5%
ValueCountFrequency (%)
034859
98.9%
0.0154
 
0.2%
0.0251
 
0.1%
0.0331
 
0.1%
0.0417
 
< 0.1%
ValueCountFrequency (%)
6.671
< 0.1%
5.811
< 0.1%
5.431
< 0.1%
5.131
< 0.1%
2.471
< 0.1%

Unnamed: 39
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
4
35256 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters35256
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4
ValueCountFrequency (%)
435256
100.0%
2021-02-18T22:26:25.051272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:25.114868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
435256
100.0%

Most occurring characters

ValueCountFrequency (%)
435256
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number35256
100.0%

Most frequent character per category

ValueCountFrequency (%)
435256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common35256
100.0%

Most frequent character per script

ValueCountFrequency (%)
435256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII35256
100.0%

Most frequent character per block

ValueCountFrequency (%)
435256
100.0%

Unnamed: 40
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
PARTI PIRATE
35256 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters423072
Distinct characters7
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPARTI PIRATE
2nd rowPARTI PIRATE
3rd rowPARTI PIRATE
4th rowPARTI PIRATE
5th rowPARTI PIRATE
ValueCountFrequency (%)
PARTI PIRATE35256
100.0%
2021-02-18T22:26:25.251137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:25.305887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
parti35256
50.0%
pirate35256
50.0%

Most occurring characters

ValueCountFrequency (%)
P70512
16.7%
A70512
16.7%
R70512
16.7%
T70512
16.7%
I70512
16.7%
35256
8.3%
E35256
8.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter387816
91.7%
Space Separator35256
 
8.3%

Most frequent character per category

ValueCountFrequency (%)
P70512
18.2%
A70512
18.2%
R70512
18.2%
T70512
18.2%
I70512
18.2%
E35256
9.1%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin387816
91.7%
Common35256
 
8.3%

Most frequent character per script

ValueCountFrequency (%)
P70512
18.2%
A70512
18.2%
R70512
18.2%
T70512
18.2%
I70512
18.2%
E35256
9.1%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII423072
100.0%

Most frequent character per block

ValueCountFrequency (%)
P70512
16.7%
A70512
16.7%
R70512
16.7%
T70512
16.7%
I70512
16.7%
35256
8.3%
E35256
8.3%

Unnamed: 41
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
PARTI PIRATE
35256 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters423072
Distinct characters7
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPARTI PIRATE
2nd rowPARTI PIRATE
3rd rowPARTI PIRATE
4th rowPARTI PIRATE
5th rowPARTI PIRATE
ValueCountFrequency (%)
PARTI PIRATE35256
100.0%
2021-02-18T22:26:25.437910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:25.491843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
parti35256
50.0%
pirate35256
50.0%

Most occurring characters

ValueCountFrequency (%)
P70512
16.7%
A70512
16.7%
R70512
16.7%
T70512
16.7%
I70512
16.7%
35256
8.3%
E35256
8.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter387816
91.7%
Space Separator35256
 
8.3%

Most frequent character per category

ValueCountFrequency (%)
P70512
18.2%
A70512
18.2%
R70512
18.2%
T70512
18.2%
I70512
18.2%
E35256
9.1%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin387816
91.7%
Common35256
 
8.3%

Most frequent character per script

ValueCountFrequency (%)
P70512
18.2%
A70512
18.2%
R70512
18.2%
T70512
18.2%
I70512
18.2%
E35256
9.1%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII423072
100.0%

Most frequent character per block

ValueCountFrequency (%)
P70512
16.7%
A70512
16.7%
R70512
16.7%
T70512
16.7%
I70512
16.7%
35256
8.3%
E35256
8.3%

Unnamed: 42
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
MARIE Florie
35256 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters423072
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMARIE Florie
2nd rowMARIE Florie
3rd rowMARIE Florie
4th rowMARIE Florie
5th rowMARIE Florie
ValueCountFrequency (%)
MARIE Florie35256
100.0%
2021-02-18T22:26:25.625375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:25.679902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
florie35256
50.0%
marie35256
50.0%

Most occurring characters

ValueCountFrequency (%)
M35256
8.3%
A35256
8.3%
R35256
8.3%
I35256
8.3%
E35256
8.3%
35256
8.3%
F35256
8.3%
l35256
8.3%
o35256
8.3%
r35256
8.3%
Other values (2)70512
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter211536
50.0%
Lowercase Letter176280
41.7%
Space Separator35256
 
8.3%

Most frequent character per category

ValueCountFrequency (%)
M35256
16.7%
A35256
16.7%
R35256
16.7%
I35256
16.7%
E35256
16.7%
F35256
16.7%
ValueCountFrequency (%)
l35256
20.0%
o35256
20.0%
r35256
20.0%
i35256
20.0%
e35256
20.0%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin387816
91.7%
Common35256
 
8.3%

Most frequent character per script

ValueCountFrequency (%)
M35256
9.1%
A35256
9.1%
R35256
9.1%
I35256
9.1%
E35256
9.1%
F35256
9.1%
l35256
9.1%
o35256
9.1%
r35256
9.1%
i35256
9.1%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII423072
100.0%

Most frequent character per block

ValueCountFrequency (%)
M35256
8.3%
A35256
8.3%
R35256
8.3%
I35256
8.3%
E35256
8.3%
35256
8.3%
F35256
8.3%
l35256
8.3%
o35256
8.3%
r35256
8.3%
Other values (2)70512
16.7%

Unnamed: 43
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct108
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.853897209
Minimum0
Maximum3062
Zeros29709
Zeros (%)84.3%
Memory size275.6 KiB
2021-02-18T22:26:25.744595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum3062
Range3062
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.62670062
Coefficient of variation (CV)21.81375044
Kurtosis20865.9502
Mean0.853897209
Median Absolute Deviation (MAD)0
Skewness131.3630004
Sum30105
Variance346.953976
MonotocityNot monotonic
2021-02-18T22:26:25.852055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
029709
84.3%
12946
 
8.4%
21092
 
3.1%
3448
 
1.3%
4242
 
0.7%
5125
 
0.4%
682
 
0.2%
780
 
0.2%
857
 
0.2%
941
 
0.1%
Other values (98)434
 
1.2%
ValueCountFrequency (%)
029709
84.3%
12946
 
8.4%
21092
 
3.1%
3448
 
1.3%
4242
 
0.7%
ValueCountFrequency (%)
30621
< 0.1%
7661
< 0.1%
6961
< 0.1%
4901
< 0.1%
4401
< 0.1%

Unnamed: 44
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct189
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0341039823
Minimum0
Maximum5.56
Zeros29721
Zeros (%)84.3%
Memory size275.6 KiB
2021-02-18T22:26:25.961991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.19
Maximum5.56
Range5.56
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1537826457
Coefficient of variation (CV)4.509228404
Kurtosis190.9293972
Mean0.0341039823
Median Absolute Deviation (MAD)0
Skewness10.7084569
Sum1202.37
Variance0.02364910211
MonotocityNot monotonic
2021-02-18T22:26:26.063808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
029721
84.3%
0.03377
 
1.1%
0.04372
 
1.1%
0.02342
 
1.0%
0.05286
 
0.8%
0.06258
 
0.7%
0.07243
 
0.7%
0.01222
 
0.6%
0.08209
 
0.6%
0.09206
 
0.6%
Other values (179)3020
 
8.6%
ValueCountFrequency (%)
029721
84.3%
0.01222
 
0.6%
0.02342
 
1.0%
0.03377
 
1.1%
0.04372
 
1.1%
ValueCountFrequency (%)
5.561
< 0.1%
51
< 0.1%
41
< 0.1%
3.851
< 0.1%
3.371
< 0.1%

Unnamed: 45
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct270
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06395195144
Minimum0
Maximum9.38
Zeros29709
Zeros (%)84.3%
Memory size275.6 KiB
2021-02-18T22:26:26.172152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.37
Maximum9.38
Range9.38
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2769698918
Coefficient of variation (CV)4.330906024
Kurtosis174.7680834
Mean0.06395195144
Median Absolute Deviation (MAD)0
Skewness10.17273036
Sum2254.69
Variance0.07671232094
MonotocityNot monotonic
2021-02-18T22:26:26.278332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
029709
84.3%
0.08216
 
0.6%
0.06191
 
0.5%
0.07180
 
0.5%
0.05177
 
0.5%
0.03167
 
0.5%
0.1159
 
0.5%
0.09158
 
0.4%
0.04156
 
0.4%
0.11145
 
0.4%
Other values (260)3998
 
11.3%
ValueCountFrequency (%)
029709
84.3%
0.0120
 
0.1%
0.0290
 
0.3%
0.03167
 
0.5%
0.04156
 
0.4%
ValueCountFrequency (%)
9.381
< 0.1%
9.091
< 0.1%
81
< 0.1%
6.431
< 0.1%
5.761
< 0.1%

Unnamed: 46
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
5
35256 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters35256
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5
ValueCountFrequency (%)
535256
100.0%
2021-02-18T22:26:26.450680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:26.502811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
535256
100.0%

Most occurring characters

ValueCountFrequency (%)
535256
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number35256
100.0%

Most frequent character per category

ValueCountFrequency (%)
535256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common35256
100.0%

Most frequent character per script

ValueCountFrequency (%)
535256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII35256
100.0%

Most frequent character per block

ValueCountFrequency (%)
535256
100.0%

Unnamed: 47
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
RENAISSANCE
35256 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters387816
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENAISSANCE
2nd rowRENAISSANCE
3rd rowRENAISSANCE
4th rowRENAISSANCE
5th rowRENAISSANCE
ValueCountFrequency (%)
RENAISSANCE35256
100.0%
2021-02-18T22:26:26.628515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:26.679811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
renaissance35256
100.0%

Most occurring characters

ValueCountFrequency (%)
E70512
18.2%
N70512
18.2%
A70512
18.2%
S70512
18.2%
R35256
9.1%
I35256
9.1%
C35256
9.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter387816
100.0%

Most frequent character per category

ValueCountFrequency (%)
E70512
18.2%
N70512
18.2%
A70512
18.2%
S70512
18.2%
R35256
9.1%
I35256
9.1%
C35256
9.1%

Most occurring scripts

ValueCountFrequency (%)
Latin387816
100.0%

Most frequent character per script

ValueCountFrequency (%)
E70512
18.2%
N70512
18.2%
A70512
18.2%
S70512
18.2%
R35256
9.1%
I35256
9.1%
C35256
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII387816
100.0%

Most frequent character per block

ValueCountFrequency (%)
E70512
18.2%
N70512
18.2%
A70512
18.2%
S70512
18.2%
R35256
9.1%
I35256
9.1%
C35256
9.1%

Unnamed: 48
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
RENAISSANCE SOUTENUE PAR LA RÉPUBLIQUE EN MARCHE, LE MODEM ET SES PARTENAIRES
35256 

Length

Max length77
Median length77
Mean length77
Min length77

Characters and Unicode

Total characters2714712
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENAISSANCE SOUTENUE PAR LA RÉPUBLIQUE EN MARCHE, LE MODEM ET SES PARTENAIRES
2nd rowRENAISSANCE SOUTENUE PAR LA RÉPUBLIQUE EN MARCHE, LE MODEM ET SES PARTENAIRES
3rd rowRENAISSANCE SOUTENUE PAR LA RÉPUBLIQUE EN MARCHE, LE MODEM ET SES PARTENAIRES
4th rowRENAISSANCE SOUTENUE PAR LA RÉPUBLIQUE EN MARCHE, LE MODEM ET SES PARTENAIRES
5th rowRENAISSANCE SOUTENUE PAR LA RÉPUBLIQUE EN MARCHE, LE MODEM ET SES PARTENAIRES
ValueCountFrequency (%)
RENAISSANCE SOUTENUE PAR LA RÉPUBLIQUE EN MARCHE, LE MODEM ET SES PARTENAIRES35256
100.0%
2021-02-18T22:26:27.165765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:27.224987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
ses35256
8.3%
modem35256
8.3%
et35256
8.3%
marche35256
8.3%
république35256
8.3%
en35256
8.3%
la35256
8.3%
par35256
8.3%
renaissance35256
8.3%
soutenue35256
8.3%
Other values (2)70512
16.7%

Most occurring characters

ValueCountFrequency (%)
E458328
16.9%
387816
14.3%
A246792
9.1%
R211536
 
7.8%
S211536
 
7.8%
N176280
 
6.5%
U141024
 
5.2%
I105768
 
3.9%
T105768
 
3.9%
P105768
 
3.9%
Other values (10)564096
20.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2291640
84.4%
Space Separator387816
 
14.3%
Other Punctuation35256
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
E458328
20.0%
A246792
10.8%
R211536
9.2%
S211536
9.2%
N176280
 
7.7%
U141024
 
6.2%
I105768
 
4.6%
T105768
 
4.6%
P105768
 
4.6%
L105768
 
4.6%
Other values (8)423072
18.5%
ValueCountFrequency (%)
387816
100.0%
ValueCountFrequency (%)
,35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2291640
84.4%
Common423072
 
15.6%

Most frequent character per script

ValueCountFrequency (%)
E458328
20.0%
A246792
10.8%
R211536
9.2%
S211536
9.2%
N176280
 
7.7%
U141024
 
6.2%
I105768
 
4.6%
T105768
 
4.6%
P105768
 
4.6%
L105768
 
4.6%
Other values (8)423072
18.5%
ValueCountFrequency (%)
387816
91.7%
,35256
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2679456
98.7%
None35256
 
1.3%

Most frequent character per block

ValueCountFrequency (%)
E458328
17.1%
387816
14.5%
A246792
9.2%
R211536
 
7.9%
S211536
 
7.9%
N176280
 
6.6%
U141024
 
5.3%
I105768
 
3.9%
T105768
 
3.9%
P105768
 
3.9%
Other values (9)528840
19.7%
ValueCountFrequency (%)
É35256
100.0%

Unnamed: 49
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
LOISEAU Nathalie
35256 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters564096
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLOISEAU Nathalie
2nd rowLOISEAU Nathalie
3rd rowLOISEAU Nathalie
4th rowLOISEAU Nathalie
5th rowLOISEAU Nathalie
ValueCountFrequency (%)
LOISEAU Nathalie35256
100.0%
2021-02-18T22:26:27.349055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:27.398515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
loiseau35256
50.0%
nathalie35256
50.0%

Most occurring characters

ValueCountFrequency (%)
a70512
 
12.5%
L35256
 
6.2%
O35256
 
6.2%
I35256
 
6.2%
S35256
 
6.2%
E35256
 
6.2%
A35256
 
6.2%
U35256
 
6.2%
35256
 
6.2%
N35256
 
6.2%
Other values (5)176280
31.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter282048
50.0%
Lowercase Letter246792
43.8%
Space Separator35256
 
6.2%

Most frequent character per category

ValueCountFrequency (%)
L35256
12.5%
O35256
12.5%
I35256
12.5%
S35256
12.5%
E35256
12.5%
A35256
12.5%
U35256
12.5%
N35256
12.5%
ValueCountFrequency (%)
a70512
28.6%
t35256
14.3%
h35256
14.3%
l35256
14.3%
i35256
14.3%
e35256
14.3%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin528840
93.8%
Common35256
 
6.2%

Most frequent character per script

ValueCountFrequency (%)
a70512
13.3%
L35256
 
6.7%
O35256
 
6.7%
I35256
 
6.7%
S35256
 
6.7%
E35256
 
6.7%
A35256
 
6.7%
U35256
 
6.7%
N35256
 
6.7%
t35256
 
6.7%
Other values (4)141024
26.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII564096
100.0%

Most frequent character per block

ValueCountFrequency (%)
a70512
 
12.5%
L35256
 
6.2%
O35256
 
6.2%
I35256
 
6.2%
S35256
 
6.2%
E35256
 
6.2%
A35256
 
6.2%
U35256
 
6.2%
35256
 
6.2%
N35256
 
6.2%
Other values (5)176280
31.2%

Unnamed: 50
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct1525
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.0610109
Minimum0
Maximum244918
Zeros152
Zeros (%)0.4%
Memory size275.6 KiB
2021-02-18T22:26:27.455952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q115
median34
Q389.25
95-th percentile475
Maximum244918
Range244918
Interquartile range (IQR)74.25

Descriptive statistics

Standard deviation1454.367339
Coefficient of variation (CV)10.09549586
Kurtosis22820.50179
Mean144.0610109
Median Absolute Deviation (MAD)24
Skewness137.9252363
Sum5079015
Variance2115184.357
MonotocityNot monotonic
2021-02-18T22:26:27.560857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11702
 
2.0%
12691
 
2.0%
10689
 
2.0%
8687
 
1.9%
9681
 
1.9%
6666
 
1.9%
13656
 
1.9%
7651
 
1.8%
14648
 
1.8%
15635
 
1.8%
Other values (1515)28550
81.0%
ValueCountFrequency (%)
0152
 
0.4%
1212
0.6%
2333
0.9%
3452
1.3%
4507
1.4%
ValueCountFrequency (%)
2449181
< 0.1%
438031
< 0.1%
436321
< 0.1%
307041
< 0.1%
257871
< 0.1%

Unnamed: 51
Real number (ℝ≥0)

Distinct2252
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.25492682
Minimum0
Maximum56.38
Zeros152
Zeros (%)0.4%
Memory size275.6 KiB
2021-02-18T22:26:27.662633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.48
Q17.69
median9.92
Q312.37
95-th percentile17.13
Maximum56.38
Range56.38
Interquartile range (IQR)4.68

Descriptive statistics

Standard deviation4.002167141
Coefficient of variation (CV)0.3902677426
Kurtosis3.86320646
Mean10.25492682
Median Absolute Deviation (MAD)2.32
Skewness0.9802733131
Sum361547.7
Variance16.01734182
MonotocityNot monotonic
2021-02-18T22:26:27.754757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10171
 
0.5%
12.5167
 
0.5%
11.11158
 
0.4%
0152
 
0.4%
7.69137
 
0.4%
8.33133
 
0.4%
9.09125
 
0.4%
10.5395
 
0.3%
14.2994
 
0.3%
7.1493
 
0.3%
Other values (2242)33931
96.2%
ValueCountFrequency (%)
0152
0.4%
0.241
 
< 0.1%
0.261
 
< 0.1%
0.281
 
< 0.1%
0.311
 
< 0.1%
ValueCountFrequency (%)
56.381
< 0.1%
48.841
< 0.1%
45.161
< 0.1%
43.891
< 0.1%
43.331
< 0.1%

Unnamed: 52
Real number (ℝ≥0)

Distinct3215
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.23917376
Minimum0
Maximum100
Zeros152
Zeros (%)0.4%
Memory size275.6 KiB
2021-02-18T22:26:27.855537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.01
Q114.89
median18.87
Q323.08
95-th percentile30.51
Maximum100
Range100
Interquartile range (IQR)8.19

Descriptive statistics

Standard deviation6.855936885
Coefficient of variation (CV)0.3563529791
Kurtosis4.377558673
Mean19.23917376
Median Absolute Deviation (MAD)4.08
Skewness0.8226375493
Sum678296.31
Variance47.00387057
MonotocityNot monotonic
2021-02-18T22:26:27.955614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20336
 
1.0%
16.67234
 
0.7%
25219
 
0.6%
14.29215
 
0.6%
12.5168
 
0.5%
0152
 
0.4%
18.18144
 
0.4%
22.22138
 
0.4%
15.38136
 
0.4%
11.11115
 
0.3%
Other values (3205)33399
94.7%
ValueCountFrequency (%)
0152
0.4%
0.831
 
< 0.1%
1.081
 
< 0.1%
1.151
 
< 0.1%
1.31
 
< 0.1%
ValueCountFrequency (%)
1001
< 0.1%
93.61
< 0.1%
901
< 0.1%
81.441
< 0.1%
81.091
< 0.1%

Unnamed: 53
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
6
35256 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters35256
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6
2nd row6
3rd row6
4th row6
5th row6
ValueCountFrequency (%)
635256
100.0%
2021-02-18T22:26:28.115517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:28.163891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
635256
100.0%

Most occurring characters

ValueCountFrequency (%)
635256
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number35256
100.0%

Most frequent character per category

ValueCountFrequency (%)
635256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common35256
100.0%

Most frequent character per script

ValueCountFrequency (%)
635256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII35256
100.0%

Most frequent character per block

ValueCountFrequency (%)
635256
100.0%

Unnamed: 54
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
DÉMOCRATIE REPRÉSENTATIVE
35256 

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters881400
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDÉMOCRATIE REPRÉSENTATIVE
2nd rowDÉMOCRATIE REPRÉSENTATIVE
3rd rowDÉMOCRATIE REPRÉSENTATIVE
4th rowDÉMOCRATIE REPRÉSENTATIVE
5th rowDÉMOCRATIE REPRÉSENTATIVE
ValueCountFrequency (%)
DÉMOCRATIE REPRÉSENTATIVE35256
100.0%
2021-02-18T22:26:28.283257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:28.332740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
démocratie35256
50.0%
représentative35256
50.0%

Most occurring characters

ValueCountFrequency (%)
E141024
16.0%
R105768
12.0%
T105768
12.0%
É70512
 
8.0%
A70512
 
8.0%
I70512
 
8.0%
D35256
 
4.0%
M35256
 
4.0%
O35256
 
4.0%
C35256
 
4.0%
Other values (5)176280
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter846144
96.0%
Space Separator35256
 
4.0%

Most frequent character per category

ValueCountFrequency (%)
E141024
16.7%
R105768
12.5%
T105768
12.5%
É70512
8.3%
A70512
8.3%
I70512
8.3%
D35256
 
4.2%
M35256
 
4.2%
O35256
 
4.2%
C35256
 
4.2%
Other values (4)141024
16.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin846144
96.0%
Common35256
 
4.0%

Most frequent character per script

ValueCountFrequency (%)
E141024
16.7%
R105768
12.5%
T105768
12.5%
É70512
8.3%
A70512
8.3%
I70512
8.3%
D35256
 
4.2%
M35256
 
4.2%
O35256
 
4.2%
C35256
 
4.2%
Other values (4)141024
16.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII810888
92.0%
None70512
 
8.0%

Most frequent character per block

ValueCountFrequency (%)
E141024
17.4%
R105768
13.0%
T105768
13.0%
A70512
8.7%
I70512
8.7%
D35256
 
4.3%
M35256
 
4.3%
O35256
 
4.3%
C35256
 
4.3%
35256
 
4.3%
Other values (4)141024
17.4%
ValueCountFrequency (%)
É70512
100.0%

Unnamed: 55
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
DÉMOCRATIE REPRÉSENTATIVE
35256 

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters881400
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDÉMOCRATIE REPRÉSENTATIVE
2nd rowDÉMOCRATIE REPRÉSENTATIVE
3rd rowDÉMOCRATIE REPRÉSENTATIVE
4th rowDÉMOCRATIE REPRÉSENTATIVE
5th rowDÉMOCRATIE REPRÉSENTATIVE
ValueCountFrequency (%)
DÉMOCRATIE REPRÉSENTATIVE35256
100.0%
2021-02-18T22:26:28.455739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:28.508082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
démocratie35256
50.0%
représentative35256
50.0%

Most occurring characters

ValueCountFrequency (%)
E141024
16.0%
R105768
12.0%
T105768
12.0%
É70512
 
8.0%
A70512
 
8.0%
I70512
 
8.0%
D35256
 
4.0%
M35256
 
4.0%
O35256
 
4.0%
C35256
 
4.0%
Other values (5)176280
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter846144
96.0%
Space Separator35256
 
4.0%

Most frequent character per category

ValueCountFrequency (%)
E141024
16.7%
R105768
12.5%
T105768
12.5%
É70512
8.3%
A70512
8.3%
I70512
8.3%
D35256
 
4.2%
M35256
 
4.2%
O35256
 
4.2%
C35256
 
4.2%
Other values (4)141024
16.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin846144
96.0%
Common35256
 
4.0%

Most frequent character per script

ValueCountFrequency (%)
E141024
16.7%
R105768
12.5%
T105768
12.5%
É70512
8.3%
A70512
8.3%
I70512
8.3%
D35256
 
4.2%
M35256
 
4.2%
O35256
 
4.2%
C35256
 
4.2%
Other values (4)141024
16.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII810888
92.0%
None70512
 
8.0%

Most frequent character per block

ValueCountFrequency (%)
E141024
17.4%
R105768
13.0%
T105768
13.0%
A70512
8.7%
I70512
8.7%
D35256
 
4.3%
M35256
 
4.3%
O35256
 
4.3%
C35256
 
4.3%
35256
 
4.3%
Other values (4)141024
17.4%
ValueCountFrequency (%)
É70512
100.0%

Unnamed: 56
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
TRAORÉ Hamada
35256 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters458328
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTRAORÉ Hamada
2nd rowTRAORÉ Hamada
3rd rowTRAORÉ Hamada
4th rowTRAORÉ Hamada
5th rowTRAORÉ Hamada
ValueCountFrequency (%)
TRAORÉ Hamada35256
100.0%
2021-02-18T22:26:28.654149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:28.718312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
hamada35256
50.0%
traoré35256
50.0%

Most occurring characters

ValueCountFrequency (%)
a105768
23.1%
R70512
15.4%
T35256
 
7.7%
A35256
 
7.7%
O35256
 
7.7%
É35256
 
7.7%
35256
 
7.7%
H35256
 
7.7%
m35256
 
7.7%
d35256
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter246792
53.8%
Lowercase Letter176280
38.5%
Space Separator35256
 
7.7%

Most frequent character per category

ValueCountFrequency (%)
R70512
28.6%
T35256
14.3%
A35256
14.3%
O35256
14.3%
É35256
14.3%
H35256
14.3%
ValueCountFrequency (%)
a105768
60.0%
m35256
 
20.0%
d35256
 
20.0%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin423072
92.3%
Common35256
 
7.7%

Most frequent character per script

ValueCountFrequency (%)
a105768
25.0%
R70512
16.7%
T35256
 
8.3%
A35256
 
8.3%
O35256
 
8.3%
É35256
 
8.3%
H35256
 
8.3%
m35256
 
8.3%
d35256
 
8.3%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII423072
92.3%
None35256
 
7.7%

Most frequent character per block

ValueCountFrequency (%)
a105768
25.0%
R70512
16.7%
T35256
 
8.3%
A35256
 
8.3%
O35256
 
8.3%
35256
 
8.3%
H35256
 
8.3%
m35256
 
8.3%
d35256
 
8.3%
ValueCountFrequency (%)
É35256
100.0%

Unnamed: 57
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct40
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08747447243
Minimum0
Maximum408
Zeros34697
Zeros (%)98.4%
Memory size275.6 KiB
2021-02-18T22:26:28.786509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum408
Range408
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.707780061
Coefficient of variation (CV)30.9550888
Kurtosis15140.09285
Mean0.08747447243
Median Absolute Deviation (MAD)0
Skewness108.3695856
Sum3084
Variance7.332072861
MonotocityNot monotonic
2021-02-18T22:26:28.888687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
034697
98.4%
1307
 
0.9%
299
 
0.3%
331
 
0.1%
420
 
0.1%
517
 
< 0.1%
78
 
< 0.1%
117
 
< 0.1%
96
 
< 0.1%
185
 
< 0.1%
Other values (30)59
 
0.2%
ValueCountFrequency (%)
034697
98.4%
1307
 
0.9%
299
 
0.3%
331
 
0.1%
420
 
0.1%
ValueCountFrequency (%)
4081
< 0.1%
1691
< 0.1%
841
< 0.1%
801
< 0.1%
701
< 0.1%

Unnamed: 58
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct66
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001877127297
Minimum0
Maximum4.41
Zeros34760
Zeros (%)98.6%
Memory size275.6 KiB
2021-02-18T22:26:29.003634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4.41
Range4.41
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.04447789484
Coefficient of variation (CV)23.69466093
Kurtosis4635.099454
Mean0.001877127297
Median Absolute Deviation (MAD)0
Skewness59.54510828
Sum66.18
Variance0.001978283129
MonotocityNot monotonic
2021-02-18T22:26:29.108694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034760
98.6%
0.01119
 
0.3%
0.0266
 
0.2%
0.0343
 
0.1%
0.0429
 
0.1%
0.0528
 
0.1%
0.0621
 
0.1%
0.0920
 
0.1%
0.0719
 
0.1%
0.0815
 
< 0.1%
Other values (56)136
 
0.4%
ValueCountFrequency (%)
034760
98.6%
0.01119
 
0.3%
0.0266
 
0.2%
0.0343
 
0.1%
0.0429
 
0.1%
ValueCountFrequency (%)
4.411
< 0.1%
3.451
< 0.1%
2.941
< 0.1%
1.861
< 0.1%
1.81
< 0.1%

Unnamed: 59
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct95
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003810982528
Minimum0
Maximum10.71
Zeros34718
Zeros (%)98.5%
Memory size275.6 KiB
2021-02-18T22:26:29.218085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10.71
Range10.71
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.08923856603
Coefficient of variation (CV)23.41615722
Kurtosis6889.245278
Mean0.003810982528
Median Absolute Deviation (MAD)0
Skewness69.84504059
Sum134.36
Variance0.007963521667
MonotocityNot monotonic
2021-02-18T22:26:29.330467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034718
98.5%
0.0172
 
0.2%
0.0349
 
0.1%
0.0249
 
0.1%
0.0433
 
0.1%
0.0623
 
0.1%
0.0522
 
0.1%
0.0918
 
0.1%
0.0817
 
< 0.1%
0.0717
 
< 0.1%
Other values (85)238
 
0.7%
ValueCountFrequency (%)
034718
98.5%
0.0172
 
0.2%
0.0249
 
0.1%
0.0349
 
0.1%
0.0433
 
0.1%
ValueCountFrequency (%)
10.711
< 0.1%
5.661
< 0.1%
5.261
< 0.1%
3.671
< 0.1%
3.031
< 0.1%

Unnamed: 60
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
7
35256 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters35256
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7
2nd row7
3rd row7
4th row7
5th row7
ValueCountFrequency (%)
735256
100.0%
2021-02-18T22:26:29.510855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:29.563046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
735256
100.0%

Most occurring characters

ValueCountFrequency (%)
735256
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number35256
100.0%

Most frequent character per category

ValueCountFrequency (%)
735256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common35256
100.0%

Most frequent character per script

ValueCountFrequency (%)
735256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII35256
100.0%

Most frequent character per block

ValueCountFrequency (%)
735256
100.0%

Unnamed: 61
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
ENSEMBLE PATRIOTES
35256 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters634608
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowENSEMBLE PATRIOTES
2nd rowENSEMBLE PATRIOTES
3rd rowENSEMBLE PATRIOTES
4th rowENSEMBLE PATRIOTES
5th rowENSEMBLE PATRIOTES
ValueCountFrequency (%)
ENSEMBLE PATRIOTES35256
100.0%
2021-02-18T22:26:29.692683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:29.747150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
ensemble35256
50.0%
patriotes35256
50.0%

Most occurring characters

ValueCountFrequency (%)
E141024
22.2%
S70512
11.1%
T70512
11.1%
N35256
 
5.6%
M35256
 
5.6%
B35256
 
5.6%
L35256
 
5.6%
35256
 
5.6%
P35256
 
5.6%
A35256
 
5.6%
Other values (3)105768
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter599352
94.4%
Space Separator35256
 
5.6%

Most frequent character per category

ValueCountFrequency (%)
E141024
23.5%
S70512
11.8%
T70512
11.8%
N35256
 
5.9%
M35256
 
5.9%
B35256
 
5.9%
L35256
 
5.9%
P35256
 
5.9%
A35256
 
5.9%
R35256
 
5.9%
Other values (2)70512
11.8%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin599352
94.4%
Common35256
 
5.6%

Most frequent character per script

ValueCountFrequency (%)
E141024
23.5%
S70512
11.8%
T70512
11.8%
N35256
 
5.9%
M35256
 
5.9%
B35256
 
5.9%
L35256
 
5.9%
P35256
 
5.9%
A35256
 
5.9%
R35256
 
5.9%
Other values (2)70512
11.8%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII634608
100.0%

Most frequent character per block

ValueCountFrequency (%)
E141024
22.2%
S70512
11.1%
T70512
11.1%
N35256
 
5.6%
M35256
 
5.6%
B35256
 
5.6%
L35256
 
5.6%
35256
 
5.6%
P35256
 
5.6%
A35256
 
5.6%
Other values (3)105768
16.7%

Unnamed: 62
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
ENSEMBLE PATRIOTES ET GILETS JAUNES : POUR LA FRANCE, SORTONS DE L'UNION EUROPÉENNE !
35256 

Length

Max length85
Median length85
Mean length85
Min length85

Characters and Unicode

Total characters2996760
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowENSEMBLE PATRIOTES ET GILETS JAUNES : POUR LA FRANCE, SORTONS DE L'UNION EUROPÉENNE !
2nd rowENSEMBLE PATRIOTES ET GILETS JAUNES : POUR LA FRANCE, SORTONS DE L'UNION EUROPÉENNE !
3rd rowENSEMBLE PATRIOTES ET GILETS JAUNES : POUR LA FRANCE, SORTONS DE L'UNION EUROPÉENNE !
4th rowENSEMBLE PATRIOTES ET GILETS JAUNES : POUR LA FRANCE, SORTONS DE L'UNION EUROPÉENNE !
5th rowENSEMBLE PATRIOTES ET GILETS JAUNES : POUR LA FRANCE, SORTONS DE L'UNION EUROPÉENNE !
ValueCountFrequency (%)
ENSEMBLE PATRIOTES ET GILETS JAUNES : POUR LA FRANCE, SORTONS DE L'UNION EUROPÉENNE !35256
100.0%
2021-02-18T22:26:29.880863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:29.935679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
70512
14.3%
de35256
 
7.1%
l'union35256
 
7.1%
gilets35256
 
7.1%
france35256
 
7.1%
et35256
 
7.1%
ensemble35256
 
7.1%
la35256
 
7.1%
pour35256
 
7.1%
patriotes35256
 
7.1%
Other values (3)105768
21.4%

Most occurring characters

ValueCountFrequency (%)
458328
15.3%
E423072
14.1%
N282048
9.4%
S211536
 
7.1%
O211536
 
7.1%
T176280
 
5.9%
R176280
 
5.9%
L141024
 
4.7%
A141024
 
4.7%
U141024
 
4.7%
Other values (14)634608
21.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2397408
80.0%
Space Separator458328
 
15.3%
Other Punctuation141024
 
4.7%

Most frequent character per category

ValueCountFrequency (%)
E423072
17.6%
N282048
11.8%
S211536
8.8%
O211536
8.8%
T176280
7.4%
R176280
7.4%
L141024
 
5.9%
A141024
 
5.9%
U141024
 
5.9%
P105768
 
4.4%
Other values (9)387816
16.2%
ValueCountFrequency (%)
:35256
25.0%
,35256
25.0%
'35256
25.0%
!35256
25.0%
ValueCountFrequency (%)
458328
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2397408
80.0%
Common599352
 
20.0%

Most frequent character per script

ValueCountFrequency (%)
E423072
17.6%
N282048
11.8%
S211536
8.8%
O211536
8.8%
T176280
7.4%
R176280
7.4%
L141024
 
5.9%
A141024
 
5.9%
U141024
 
5.9%
P105768
 
4.4%
Other values (9)387816
16.2%
ValueCountFrequency (%)
458328
76.5%
:35256
 
5.9%
,35256
 
5.9%
'35256
 
5.9%
!35256
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2961504
98.8%
None35256
 
1.2%

Most frequent character per block

ValueCountFrequency (%)
458328
15.5%
E423072
14.3%
N282048
9.5%
S211536
 
7.1%
O211536
 
7.1%
T176280
 
6.0%
R176280
 
6.0%
L141024
 
4.8%
A141024
 
4.8%
U141024
 
4.8%
Other values (13)599352
20.2%
ValueCountFrequency (%)
É35256
100.0%

Unnamed: 63
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
PHILIPPOT Florian
35256 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters599352
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPHILIPPOT Florian
2nd rowPHILIPPOT Florian
3rd rowPHILIPPOT Florian
4th rowPHILIPPOT Florian
5th rowPHILIPPOT Florian
ValueCountFrequency (%)
PHILIPPOT Florian35256
100.0%
2021-02-18T22:26:30.069001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:30.122078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
florian35256
50.0%
philippot35256
50.0%

Most occurring characters

ValueCountFrequency (%)
P105768
17.6%
I70512
11.8%
H35256
 
5.9%
L35256
 
5.9%
O35256
 
5.9%
T35256
 
5.9%
35256
 
5.9%
F35256
 
5.9%
l35256
 
5.9%
o35256
 
5.9%
Other values (4)141024
23.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter352560
58.8%
Lowercase Letter211536
35.3%
Space Separator35256
 
5.9%

Most frequent character per category

ValueCountFrequency (%)
P105768
30.0%
I70512
20.0%
H35256
 
10.0%
L35256
 
10.0%
O35256
 
10.0%
T35256
 
10.0%
F35256
 
10.0%
ValueCountFrequency (%)
l35256
16.7%
o35256
16.7%
r35256
16.7%
i35256
16.7%
a35256
16.7%
n35256
16.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin564096
94.1%
Common35256
 
5.9%

Most frequent character per script

ValueCountFrequency (%)
P105768
18.8%
I70512
12.5%
H35256
 
6.2%
L35256
 
6.2%
O35256
 
6.2%
T35256
 
6.2%
F35256
 
6.2%
l35256
 
6.2%
o35256
 
6.2%
r35256
 
6.2%
Other values (3)105768
18.8%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII599352
100.0%

Most frequent character per block

ValueCountFrequency (%)
P105768
17.6%
I70512
11.8%
H35256
 
5.9%
L35256
 
5.9%
O35256
 
5.9%
T35256
 
5.9%
35256
 
5.9%
F35256
 
5.9%
l35256
 
5.9%
o35256
 
5.9%
Other values (4)141024
23.5%

Unnamed: 64
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct164
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.173474019
Minimum0
Maximum1514
Zeros11587
Zeros (%)32.9%
Memory size275.6 KiB
2021-02-18T22:26:30.185947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile16
Maximum1514
Range1514
Interquartile range (IQR)4

Descriptive statistics

Standard deviation16.04849854
Coefficient of variation (CV)3.845357241
Kurtosis3593.033826
Mean4.173474019
Median Absolute Deviation (MAD)1
Skewness45.01829669
Sum147140
Variance257.5543053
MonotocityNot monotonic
2021-02-18T22:26:30.295882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
011587
32.9%
16930
19.7%
24470
 
12.7%
32957
 
8.4%
41973
 
5.6%
51323
 
3.8%
6982
 
2.8%
7709
 
2.0%
8561
 
1.6%
9447
 
1.3%
Other values (154)3317
 
9.4%
ValueCountFrequency (%)
011587
32.9%
16930
19.7%
24470
 
12.7%
32957
 
8.4%
41973
 
5.6%
ValueCountFrequency (%)
15141
< 0.1%
13141
< 0.1%
5771
< 0.1%
3981
< 0.1%
3501
< 0.1%

Unnamed: 65
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct391
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4306739278
Minimum0
Maximum13.64
Zeros11590
Zeros (%)32.9%
Memory size275.6 KiB
2021-02-18T22:26:30.410511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.29
Q30.61
95-th percentile1.42
Maximum13.64
Range13.64
Interquartile range (IQR)0.61

Descriptive statistics

Standard deviation0.5926009627
Coefficient of variation (CV)1.375985228
Kurtosis42.98845472
Mean0.4306739278
Median Absolute Deviation (MAD)0.29
Skewness4.350767843
Sum15183.84
Variance0.3511759009
MonotocityNot monotonic
2021-02-18T22:26:30.517550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
011590
32.9%
0.4362
 
1.0%
0.26347
 
1.0%
0.27342
 
1.0%
0.25341
 
1.0%
0.32341
 
1.0%
0.33339
 
1.0%
0.28333
 
0.9%
0.35333
 
0.9%
0.22332
 
0.9%
Other values (381)20596
58.4%
ValueCountFrequency (%)
011590
32.9%
0.0115
 
< 0.1%
0.0217
 
< 0.1%
0.0333
 
0.1%
0.0436
 
0.1%
ValueCountFrequency (%)
13.641
 
< 0.1%
11.361
 
< 0.1%
11.113
< 0.1%
101
 
< 0.1%
9.381
 
< 0.1%

Unnamed: 66
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct537
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8105394826
Minimum0
Maximum18.75
Zeros11587
Zeros (%)32.9%
Memory size275.6 KiB
2021-02-18T22:26:30.631969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.57
Q31.17
95-th percentile2.63
Maximum18.75
Range18.75
Interquartile range (IQR)1.17

Descriptive statistics

Standard deviation1.054043361
Coefficient of variation (CV)1.300421983
Kurtosis27.34195279
Mean0.8105394826
Median Absolute Deviation (MAD)0.57
Skewness3.540024087
Sum28576.38
Variance1.111007407
MonotocityNot monotonic
2021-02-18T22:26:30.740505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
011587
32.9%
0.69194
 
0.6%
0.76190
 
0.5%
0.85189
 
0.5%
0.75188
 
0.5%
0.65188
 
0.5%
0.68184
 
0.5%
0.93182
 
0.5%
0.51182
 
0.5%
0.72182
 
0.5%
Other values (527)21990
62.4%
ValueCountFrequency (%)
011587
32.9%
0.022
 
< 0.1%
0.033
 
< 0.1%
0.042
 
< 0.1%
0.0511
 
< 0.1%
ValueCountFrequency (%)
18.751
< 0.1%
18.181
< 0.1%
17.861
< 0.1%
17.651
< 0.1%
16.671
< 0.1%

Unnamed: 67
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
8
35256 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters35256
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8
ValueCountFrequency (%)
835256
100.0%
2021-02-18T22:26:30.920975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:30.973102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
835256
100.0%

Most occurring characters

ValueCountFrequency (%)
835256
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number35256
100.0%

Most frequent character per category

ValueCountFrequency (%)
835256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common35256
100.0%

Most frequent character per script

ValueCountFrequency (%)
835256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII35256
100.0%

Most frequent character per block

ValueCountFrequency (%)
835256
100.0%

Unnamed: 68
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
PACE
35256 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters141024
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPACE
2nd rowPACE
3rd rowPACE
4th rowPACE
5th rowPACE
ValueCountFrequency (%)
PACE35256
100.0%
2021-02-18T22:26:31.101049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:31.153296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
pace35256
100.0%

Most occurring characters

ValueCountFrequency (%)
P35256
25.0%
A35256
25.0%
C35256
25.0%
E35256
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter141024
100.0%

Most frequent character per category

ValueCountFrequency (%)
P35256
25.0%
A35256
25.0%
C35256
25.0%
E35256
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin141024
100.0%

Most frequent character per script

ValueCountFrequency (%)
P35256
25.0%
A35256
25.0%
C35256
25.0%
E35256
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII141024
100.0%

Most frequent character per block

ValueCountFrequency (%)
P35256
25.0%
A35256
25.0%
C35256
25.0%
E35256
25.0%

Unnamed: 69
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
PACE - PARTI DES CITOYENS EUROPÉENS
35256 

Length

Max length35
Median length35
Mean length35
Min length35

Characters and Unicode

Total characters1233960
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPACE - PARTI DES CITOYENS EUROPÉENS
2nd rowPACE - PARTI DES CITOYENS EUROPÉENS
3rd rowPACE - PARTI DES CITOYENS EUROPÉENS
4th rowPACE - PARTI DES CITOYENS EUROPÉENS
5th rowPACE - PARTI DES CITOYENS EUROPÉENS
ValueCountFrequency (%)
PACE - PARTI DES CITOYENS EUROPÉENS35256
100.0%
2021-02-18T22:26:31.282271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:31.335592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
parti35256
16.7%
citoyens35256
16.7%
des35256
16.7%
pace35256
16.7%
européens35256
16.7%
35256
16.7%

Most occurring characters

ValueCountFrequency (%)
E176280
14.3%
176280
14.3%
P105768
8.6%
S105768
8.6%
A70512
 
5.7%
C70512
 
5.7%
R70512
 
5.7%
T70512
 
5.7%
I70512
 
5.7%
O70512
 
5.7%
Other values (6)246792
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1022424
82.9%
Space Separator176280
 
14.3%
Dash Punctuation35256
 
2.9%

Most frequent character per category

ValueCountFrequency (%)
E176280
17.2%
P105768
10.3%
S105768
10.3%
A70512
 
6.9%
C70512
 
6.9%
R70512
 
6.9%
T70512
 
6.9%
I70512
 
6.9%
O70512
 
6.9%
N70512
 
6.9%
Other values (4)141024
13.8%
ValueCountFrequency (%)
176280
100.0%
ValueCountFrequency (%)
-35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1022424
82.9%
Common211536
 
17.1%

Most frequent character per script

ValueCountFrequency (%)
E176280
17.2%
P105768
10.3%
S105768
10.3%
A70512
 
6.9%
C70512
 
6.9%
R70512
 
6.9%
T70512
 
6.9%
I70512
 
6.9%
O70512
 
6.9%
N70512
 
6.9%
Other values (4)141024
13.8%
ValueCountFrequency (%)
176280
83.3%
-35256
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1198704
97.1%
None35256
 
2.9%

Most frequent character per block

ValueCountFrequency (%)
E176280
14.7%
176280
14.7%
P105768
8.8%
S105768
8.8%
A70512
 
5.9%
C70512
 
5.9%
R70512
 
5.9%
T70512
 
5.9%
I70512
 
5.9%
O70512
 
5.9%
Other values (5)211536
17.6%
ValueCountFrequency (%)
É35256
100.0%

Unnamed: 70
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
ALEXANDRE Audric
35256 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters564096
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowALEXANDRE Audric
2nd rowALEXANDRE Audric
3rd rowALEXANDRE Audric
4th rowALEXANDRE Audric
5th rowALEXANDRE Audric
ValueCountFrequency (%)
ALEXANDRE Audric35256
100.0%
2021-02-18T22:26:31.465790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:31.518870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
alexandre35256
50.0%
audric35256
50.0%

Most occurring characters

ValueCountFrequency (%)
A105768
18.8%
E70512
12.5%
L35256
 
6.2%
X35256
 
6.2%
N35256
 
6.2%
D35256
 
6.2%
R35256
 
6.2%
35256
 
6.2%
u35256
 
6.2%
d35256
 
6.2%
Other values (3)105768
18.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter352560
62.5%
Lowercase Letter176280
31.2%
Space Separator35256
 
6.2%

Most frequent character per category

ValueCountFrequency (%)
A105768
30.0%
E70512
20.0%
L35256
 
10.0%
X35256
 
10.0%
N35256
 
10.0%
D35256
 
10.0%
R35256
 
10.0%
ValueCountFrequency (%)
u35256
20.0%
d35256
20.0%
r35256
20.0%
i35256
20.0%
c35256
20.0%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin528840
93.8%
Common35256
 
6.2%

Most frequent character per script

ValueCountFrequency (%)
A105768
20.0%
E70512
13.3%
L35256
 
6.7%
X35256
 
6.7%
N35256
 
6.7%
D35256
 
6.7%
R35256
 
6.7%
u35256
 
6.7%
d35256
 
6.7%
r35256
 
6.7%
Other values (2)70512
13.3%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII564096
100.0%

Most frequent character per block

ValueCountFrequency (%)
A105768
18.8%
E70512
12.5%
L35256
 
6.2%
X35256
 
6.2%
N35256
 
6.2%
D35256
 
6.2%
R35256
 
6.2%
35256
 
6.2%
u35256
 
6.2%
d35256
 
6.2%
Other values (3)105768
18.8%

Unnamed: 71
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct34
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1889891082
Minimum0
Maximum239
Zeros32323
Zeros (%)91.7%
Memory size275.6 KiB
2021-02-18T22:26:31.571435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum239
Range239
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.733955692
Coefficient of variation (CV)9.174897476
Kurtosis10353.60081
Mean0.1889891082
Median Absolute Deviation (MAD)0
Skewness80.47056907
Sum6663
Variance3.006602342
MonotocityNot monotonic
2021-02-18T22:26:31.667825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
032323
91.7%
11982
 
5.6%
2405
 
1.1%
3187
 
0.5%
497
 
0.3%
554
 
0.2%
649
 
0.1%
824
 
0.1%
924
 
0.1%
723
 
0.1%
Other values (24)88
 
0.2%
ValueCountFrequency (%)
032323
91.7%
11982
 
5.6%
2405
 
1.1%
3187
 
0.5%
497
 
0.3%
ValueCountFrequency (%)
2391
< 0.1%
671
< 0.1%
581
< 0.1%
391
< 0.1%
381
< 0.1%

Unnamed: 72
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct144
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01357499433
Minimum0
Maximum8.79
Zeros32369
Zeros (%)91.8%
Memory size275.6 KiB
2021-02-18T22:26:32.225009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.04
Maximum8.79
Range8.79
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1045138956
Coefficient of variation (CV)7.699001054
Kurtosis1657.435966
Mean0.01357499433
Median Absolute Deviation (MAD)0
Skewness28.23330732
Sum478.6
Variance0.01092315438
MonotocityNot monotonic
2021-02-18T22:26:32.328530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
032369
91.8%
0.02358
 
1.0%
0.01321
 
0.9%
0.03272
 
0.8%
0.04236
 
0.7%
0.05156
 
0.4%
0.06127
 
0.4%
0.07104
 
0.3%
0.0896
 
0.3%
0.1174
 
0.2%
Other values (134)1143
 
3.2%
ValueCountFrequency (%)
032369
91.8%
0.01321
 
0.9%
0.02358
 
1.0%
0.03272
 
0.8%
0.04236
 
0.7%
ValueCountFrequency (%)
8.791
< 0.1%
3.771
< 0.1%
3.71
< 0.1%
3.21
< 0.1%
3.031
< 0.1%

Unnamed: 73
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct195
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02582539142
Minimum0
Maximum17.2
Zeros32328
Zeros (%)91.7%
Memory size275.6 KiB
2021-02-18T22:26:32.428272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.09
Maximum17.2
Range17.2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1933021266
Coefficient of variation (CV)7.484964057
Kurtosis1970.763933
Mean0.02582539142
Median Absolute Deviation (MAD)0
Skewness30.18692025
Sum910.5
Variance0.03736571213
MonotocityNot monotonic
2021-02-18T22:26:32.528984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
032328
91.7%
0.04176
 
0.5%
0.03172
 
0.5%
0.02152
 
0.4%
0.05145
 
0.4%
0.06138
 
0.4%
0.08126
 
0.4%
0.07120
 
0.3%
0.09111
 
0.3%
0.0185
 
0.2%
Other values (185)1703
 
4.8%
ValueCountFrequency (%)
032328
91.7%
0.0185
 
0.2%
0.02152
 
0.4%
0.03172
 
0.5%
0.04176
 
0.5%
ValueCountFrequency (%)
17.21
 
< 0.1%
6.821
 
< 0.1%
6.121
 
< 0.1%
4.762
< 0.1%
4.554
< 0.1%

Unnamed: 74
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
9
35256 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters35256
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9
2nd row9
3rd row9
4th row9
5th row9
ValueCountFrequency (%)
935256
100.0%
2021-02-18T22:26:32.696644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:32.745010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
935256
100.0%

Most occurring characters

ValueCountFrequency (%)
935256
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number35256
100.0%

Most frequent character per category

ValueCountFrequency (%)
935256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common35256
100.0%

Most frequent character per script

ValueCountFrequency (%)
935256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII35256
100.0%

Most frequent character per block

ValueCountFrequency (%)
935256
100.0%

Unnamed: 75
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
URGENCE ÉCOLOGIE
35256 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters564096
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowURGENCE ÉCOLOGIE
2nd rowURGENCE ÉCOLOGIE
3rd rowURGENCE ÉCOLOGIE
4th rowURGENCE ÉCOLOGIE
5th rowURGENCE ÉCOLOGIE
ValueCountFrequency (%)
URGENCE ÉCOLOGIE35256
100.0%
2021-02-18T22:26:32.863945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:32.913327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
urgence35256
50.0%
écologie35256
50.0%

Most occurring characters

ValueCountFrequency (%)
E105768
18.8%
G70512
12.5%
C70512
12.5%
O70512
12.5%
U35256
 
6.2%
R35256
 
6.2%
N35256
 
6.2%
35256
 
6.2%
É35256
 
6.2%
L35256
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter528840
93.8%
Space Separator35256
 
6.2%

Most frequent character per category

ValueCountFrequency (%)
E105768
20.0%
G70512
13.3%
C70512
13.3%
O70512
13.3%
U35256
 
6.7%
R35256
 
6.7%
N35256
 
6.7%
É35256
 
6.7%
L35256
 
6.7%
I35256
 
6.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin528840
93.8%
Common35256
 
6.2%

Most frequent character per script

ValueCountFrequency (%)
E105768
20.0%
G70512
13.3%
C70512
13.3%
O70512
13.3%
U35256
 
6.7%
R35256
 
6.7%
N35256
 
6.7%
É35256
 
6.7%
L35256
 
6.7%
I35256
 
6.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII528840
93.8%
None35256
 
6.2%

Most frequent character per block

ValueCountFrequency (%)
E105768
20.0%
G70512
13.3%
C70512
13.3%
O70512
13.3%
U35256
 
6.7%
R35256
 
6.7%
N35256
 
6.7%
35256
 
6.7%
L35256
 
6.7%
I35256
 
6.7%
ValueCountFrequency (%)
É35256
100.0%

Unnamed: 76
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
URGENCE ÉCOLOGIE
35256 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters564096
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowURGENCE ÉCOLOGIE
2nd rowURGENCE ÉCOLOGIE
3rd rowURGENCE ÉCOLOGIE
4th rowURGENCE ÉCOLOGIE
5th rowURGENCE ÉCOLOGIE
ValueCountFrequency (%)
URGENCE ÉCOLOGIE35256
100.0%
2021-02-18T22:26:33.034029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:33.083175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
urgence35256
50.0%
écologie35256
50.0%

Most occurring characters

ValueCountFrequency (%)
E105768
18.8%
G70512
12.5%
C70512
12.5%
O70512
12.5%
U35256
 
6.2%
R35256
 
6.2%
N35256
 
6.2%
35256
 
6.2%
É35256
 
6.2%
L35256
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter528840
93.8%
Space Separator35256
 
6.2%

Most frequent character per category

ValueCountFrequency (%)
E105768
20.0%
G70512
13.3%
C70512
13.3%
O70512
13.3%
U35256
 
6.7%
R35256
 
6.7%
N35256
 
6.7%
É35256
 
6.7%
L35256
 
6.7%
I35256
 
6.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin528840
93.8%
Common35256
 
6.2%

Most frequent character per script

ValueCountFrequency (%)
E105768
20.0%
G70512
13.3%
C70512
13.3%
O70512
13.3%
U35256
 
6.7%
R35256
 
6.7%
N35256
 
6.7%
É35256
 
6.7%
L35256
 
6.7%
I35256
 
6.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII528840
93.8%
None35256
 
6.2%

Most frequent character per block

ValueCountFrequency (%)
E105768
20.0%
G70512
13.3%
C70512
13.3%
O70512
13.3%
U35256
 
6.7%
R35256
 
6.7%
N35256
 
6.7%
35256
 
6.7%
L35256
 
6.7%
I35256
 
6.7%
ValueCountFrequency (%)
É35256
100.0%

Unnamed: 77
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
BOURG Dominique
35256 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters528840
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBOURG Dominique
2nd rowBOURG Dominique
3rd rowBOURG Dominique
4th rowBOURG Dominique
5th rowBOURG Dominique
ValueCountFrequency (%)
BOURG Dominique35256
100.0%
2021-02-18T22:26:33.203524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:33.252790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
bourg35256
50.0%
dominique35256
50.0%

Most occurring characters

ValueCountFrequency (%)
i70512
13.3%
B35256
 
6.7%
O35256
 
6.7%
U35256
 
6.7%
R35256
 
6.7%
G35256
 
6.7%
35256
 
6.7%
D35256
 
6.7%
o35256
 
6.7%
m35256
 
6.7%
Other values (4)141024
26.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter282048
53.3%
Uppercase Letter211536
40.0%
Space Separator35256
 
6.7%

Most frequent character per category

ValueCountFrequency (%)
i70512
25.0%
o35256
12.5%
m35256
12.5%
n35256
12.5%
q35256
12.5%
u35256
12.5%
e35256
12.5%
ValueCountFrequency (%)
B35256
16.7%
O35256
16.7%
U35256
16.7%
R35256
16.7%
G35256
16.7%
D35256
16.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin493584
93.3%
Common35256
 
6.7%

Most frequent character per script

ValueCountFrequency (%)
i70512
14.3%
B35256
 
7.1%
O35256
 
7.1%
U35256
 
7.1%
R35256
 
7.1%
G35256
 
7.1%
D35256
 
7.1%
o35256
 
7.1%
m35256
 
7.1%
n35256
 
7.1%
Other values (3)105768
21.4%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII528840
100.0%

Most frequent character per block

ValueCountFrequency (%)
i70512
13.3%
B35256
 
6.7%
O35256
 
6.7%
U35256
 
6.7%
R35256
 
6.7%
G35256
 
6.7%
35256
 
6.7%
D35256
 
6.7%
o35256
 
6.7%
m35256
 
6.7%
Other values (4)141024
26.7%

Unnamed: 78
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct359
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.68981166
Minimum0
Maximum11770
Zeros7093
Zeros (%)20.1%
Memory size275.6 KiB
2021-02-18T22:26:33.309781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q38
95-th percentile42
Maximum11770
Range11770
Interquartile range (IQR)7

Descriptive statistics

Standard deviation79.72936171
Coefficient of variation (CV)6.820414563
Kurtosis13644.89031
Mean11.68981166
Median Absolute Deviation (MAD)3
Skewness98.04685896
Sum412136
Variance6356.771118
MonotocityNot monotonic
2021-02-18T22:26:33.412738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07093
20.1%
15556
15.8%
24012
11.4%
32905
 
8.2%
42145
 
6.1%
51747
 
5.0%
61320
 
3.7%
71091
 
3.1%
8897
 
2.5%
9749
 
2.1%
Other values (349)7741
22.0%
ValueCountFrequency (%)
07093
20.1%
15556
15.8%
24012
11.4%
32905
8.2%
42145
 
6.1%
ValueCountFrequency (%)
117701
< 0.1%
36441
< 0.1%
30061
< 0.1%
21071
< 0.1%
19401
< 0.1%

Unnamed: 79
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct485
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8599747561
Minimum0
Maximum14.81
Zeros7093
Zeros (%)20.1%
Memory size275.6 KiB
2021-02-18T22:26:33.517126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.32
median0.78
Q31.21
95-th percentile2.17
Maximum14.81
Range14.81
Interquartile range (IQR)0.89

Descriptive statistics

Standard deviation0.7901872316
Coefficient of variation (CV)0.9188493337
Kurtosis15.80796089
Mean0.8599747561
Median Absolute Deviation (MAD)0.45
Skewness2.392141485
Sum30319.27
Variance0.6243958609
MonotocityNot monotonic
2021-02-18T22:26:33.618905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07093
 
20.1%
0.9267
 
0.8%
0.68262
 
0.7%
0.79257
 
0.7%
0.83250
 
0.7%
0.78249
 
0.7%
0.81245
 
0.7%
0.93245
 
0.7%
0.85241
 
0.7%
0.8238
 
0.7%
Other values (475)25909
73.5%
ValueCountFrequency (%)
07093
20.1%
0.0112
 
< 0.1%
0.029
 
< 0.1%
0.037
 
< 0.1%
0.0414
 
< 0.1%
ValueCountFrequency (%)
14.811
< 0.1%
12.51
< 0.1%
11.761
< 0.1%
11.111
< 0.1%
10.261
< 0.1%

Unnamed: 80
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct715
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.626464432
Minimum0
Maximum37.61
Zeros7093
Zeros (%)20.1%
Memory size275.6 KiB
2021-02-18T22:26:33.734543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.62
median1.49
Q32.3
95-th percentile4
Maximum37.61
Range37.61
Interquartile range (IQR)1.68

Descriptive statistics

Standard deviation1.487236466
Coefficient of variation (CV)0.9143983954
Kurtosis46.03825055
Mean1.626464432
Median Absolute Deviation (MAD)0.84
Skewness3.480890083
Sum57342.63
Variance2.211872307
MonotocityNot monotonic
2021-02-18T22:26:33.833285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07093
 
20.1%
1.69188
 
0.5%
1.82177
 
0.5%
1.56173
 
0.5%
1.75172
 
0.5%
1.79170
 
0.5%
1.52165
 
0.5%
1.49165
 
0.5%
1.72165
 
0.5%
2.08163
 
0.5%
Other values (705)26625
75.5%
ValueCountFrequency (%)
07093
20.1%
0.034
 
< 0.1%
0.043
 
< 0.1%
0.056
 
< 0.1%
0.064
 
< 0.1%
ValueCountFrequency (%)
37.611
< 0.1%
36.91
< 0.1%
36.131
< 0.1%
32.371
< 0.1%
26.131
< 0.1%

Unnamed: 81
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
10
35256 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters70512
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10
ValueCountFrequency (%)
1035256
100.0%
2021-02-18T22:26:33.998708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:34.047244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1035256
100.0%

Most occurring characters

ValueCountFrequency (%)
135256
50.0%
035256
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number70512
100.0%

Most frequent character per category

ValueCountFrequency (%)
135256
50.0%
035256
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common70512
100.0%

Most frequent character per script

ValueCountFrequency (%)
135256
50.0%
035256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70512
100.0%

Most frequent character per block

ValueCountFrequency (%)
135256
50.0%
035256
50.0%

Unnamed: 82
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
LISTE DE LA RECONQUÊTE
35256 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters775632
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLISTE DE LA RECONQUÊTE
2nd rowLISTE DE LA RECONQUÊTE
3rd rowLISTE DE LA RECONQUÊTE
4th rowLISTE DE LA RECONQUÊTE
5th rowLISTE DE LA RECONQUÊTE
ValueCountFrequency (%)
LISTE DE LA RECONQUÊTE35256
100.0%
2021-02-18T22:26:34.167760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:34.218415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
de35256
25.0%
reconquête35256
25.0%
la35256
25.0%
liste35256
25.0%

Most occurring characters

ValueCountFrequency (%)
E141024
18.2%
105768
13.6%
L70512
 
9.1%
T70512
 
9.1%
I35256
 
4.5%
S35256
 
4.5%
D35256
 
4.5%
A35256
 
4.5%
R35256
 
4.5%
C35256
 
4.5%
Other values (5)176280
22.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter669864
86.4%
Space Separator105768
 
13.6%

Most frequent character per category

ValueCountFrequency (%)
E141024
21.1%
L70512
10.5%
T70512
10.5%
I35256
 
5.3%
S35256
 
5.3%
D35256
 
5.3%
A35256
 
5.3%
R35256
 
5.3%
C35256
 
5.3%
O35256
 
5.3%
Other values (4)141024
21.1%
ValueCountFrequency (%)
105768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin669864
86.4%
Common105768
 
13.6%

Most frequent character per script

ValueCountFrequency (%)
E141024
21.1%
L70512
10.5%
T70512
10.5%
I35256
 
5.3%
S35256
 
5.3%
D35256
 
5.3%
A35256
 
5.3%
R35256
 
5.3%
C35256
 
5.3%
O35256
 
5.3%
Other values (4)141024
21.1%
ValueCountFrequency (%)
105768
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII740376
95.5%
None35256
 
4.5%

Most frequent character per block

ValueCountFrequency (%)
E141024
19.0%
105768
14.3%
L70512
9.5%
T70512
9.5%
I35256
 
4.8%
S35256
 
4.8%
D35256
 
4.8%
A35256
 
4.8%
R35256
 
4.8%
C35256
 
4.8%
Other values (4)141024
19.0%
ValueCountFrequency (%)
Ê35256
100.0%

Unnamed: 83
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
LISTE DE LA RECONQUÊTE
35256 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters775632
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLISTE DE LA RECONQUÊTE
2nd rowLISTE DE LA RECONQUÊTE
3rd rowLISTE DE LA RECONQUÊTE
4th rowLISTE DE LA RECONQUÊTE
5th rowLISTE DE LA RECONQUÊTE
ValueCountFrequency (%)
LISTE DE LA RECONQUÊTE35256
100.0%
2021-02-18T22:26:34.343685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:34.395640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
de35256
25.0%
reconquête35256
25.0%
la35256
25.0%
liste35256
25.0%

Most occurring characters

ValueCountFrequency (%)
E141024
18.2%
105768
13.6%
L70512
 
9.1%
T70512
 
9.1%
I35256
 
4.5%
S35256
 
4.5%
D35256
 
4.5%
A35256
 
4.5%
R35256
 
4.5%
C35256
 
4.5%
Other values (5)176280
22.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter669864
86.4%
Space Separator105768
 
13.6%

Most frequent character per category

ValueCountFrequency (%)
E141024
21.1%
L70512
10.5%
T70512
10.5%
I35256
 
5.3%
S35256
 
5.3%
D35256
 
5.3%
A35256
 
5.3%
R35256
 
5.3%
C35256
 
5.3%
O35256
 
5.3%
Other values (4)141024
21.1%
ValueCountFrequency (%)
105768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin669864
86.4%
Common105768
 
13.6%

Most frequent character per script

ValueCountFrequency (%)
E141024
21.1%
L70512
10.5%
T70512
10.5%
I35256
 
5.3%
S35256
 
5.3%
D35256
 
5.3%
A35256
 
5.3%
R35256
 
5.3%
C35256
 
5.3%
O35256
 
5.3%
Other values (4)141024
21.1%
ValueCountFrequency (%)
105768
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII740376
95.5%
None35256
 
4.5%

Most frequent character per block

ValueCountFrequency (%)
E141024
19.0%
105768
14.3%
L70512
9.5%
T70512
9.5%
I35256
 
4.8%
S35256
 
4.8%
D35256
 
4.8%
A35256
 
4.8%
R35256
 
4.8%
C35256
 
4.8%
Other values (4)141024
19.0%
ValueCountFrequency (%)
Ê35256
100.0%

Unnamed: 84
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
VAUCLIN Vincent
35256 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters528840
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVAUCLIN Vincent
2nd rowVAUCLIN Vincent
3rd rowVAUCLIN Vincent
4th rowVAUCLIN Vincent
5th rowVAUCLIN Vincent
ValueCountFrequency (%)
VAUCLIN Vincent35256
100.0%
2021-02-18T22:26:34.522858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:34.574668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
vauclin35256
50.0%
vincent35256
50.0%

Most occurring characters

ValueCountFrequency (%)
V70512
13.3%
n70512
13.3%
A35256
 
6.7%
U35256
 
6.7%
C35256
 
6.7%
L35256
 
6.7%
I35256
 
6.7%
N35256
 
6.7%
35256
 
6.7%
i35256
 
6.7%
Other values (3)105768
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter282048
53.3%
Lowercase Letter211536
40.0%
Space Separator35256
 
6.7%

Most frequent character per category

ValueCountFrequency (%)
V70512
25.0%
A35256
12.5%
U35256
12.5%
C35256
12.5%
L35256
12.5%
I35256
12.5%
N35256
12.5%
ValueCountFrequency (%)
n70512
33.3%
i35256
16.7%
c35256
16.7%
e35256
16.7%
t35256
16.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin493584
93.3%
Common35256
 
6.7%

Most frequent character per script

ValueCountFrequency (%)
V70512
14.3%
n70512
14.3%
A35256
7.1%
U35256
7.1%
C35256
7.1%
L35256
7.1%
I35256
7.1%
N35256
7.1%
i35256
7.1%
c35256
7.1%
Other values (2)70512
14.3%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII528840
100.0%

Most frequent character per block

ValueCountFrequency (%)
V70512
13.3%
n70512
13.3%
A35256
 
6.7%
U35256
 
6.7%
C35256
 
6.7%
L35256
 
6.7%
I35256
 
6.7%
N35256
 
6.7%
35256
 
6.7%
i35256
 
6.7%
Other values (3)105768
20.0%

Unnamed: 85
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct39
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1295949626
Minimum0
Maximum130
Zeros33866
Zeros (%)96.1%
Memory size275.6 KiB
2021-02-18T22:26:34.629368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum130
Range130
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.581545829
Coefficient of variation (CV)12.20376007
Kurtosis2432.889033
Mean0.1295949626
Median Absolute Deviation (MAD)0
Skewness41.09332256
Sum4569
Variance2.501287211
MonotocityNot monotonic
2021-02-18T22:26:34.733611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
033866
96.1%
1733
 
2.1%
2276
 
0.8%
3110
 
0.3%
459
 
0.2%
544
 
0.1%
628
 
0.1%
826
 
0.1%
723
 
0.1%
1010
 
< 0.1%
Other values (29)81
 
0.2%
ValueCountFrequency (%)
033866
96.1%
1733
 
2.1%
2276
 
0.8%
3110
 
0.3%
459
 
0.2%
ValueCountFrequency (%)
1301
< 0.1%
1011
< 0.1%
871
< 0.1%
661
< 0.1%
651
< 0.1%

Unnamed: 86
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct124
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.007243306104
Minimum0
Maximum12.5
Zeros33928
Zeros (%)96.2%
Memory size275.6 KiB
2021-02-18T22:26:34.839426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum12.5
Range12.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1127491429
Coefficient of variation (CV)15.56597792
Kurtosis5976.260872
Mean0.007243306104
Median Absolute Deviation (MAD)0
Skewness63.96032701
Sum255.37
Variance0.01271236922
MonotocityNot monotonic
2021-02-18T22:26:34.955402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033928
96.2%
0.01203
 
0.6%
0.02152
 
0.4%
0.03116
 
0.3%
0.0483
 
0.2%
0.0577
 
0.2%
0.0764
 
0.2%
0.0661
 
0.2%
0.141
 
0.1%
0.0838
 
0.1%
Other values (114)493
 
1.4%
ValueCountFrequency (%)
033928
96.2%
0.01203
 
0.6%
0.02152
 
0.4%
0.03116
 
0.3%
0.0483
 
0.2%
ValueCountFrequency (%)
12.51
< 0.1%
9.621
< 0.1%
4.621
< 0.1%
3.751
< 0.1%
3.571
< 0.1%

Unnamed: 87
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct169
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01331603131
Minimum0
Maximum14.29
Zeros33875
Zeros (%)96.1%
Memory size275.6 KiB
2021-02-18T22:26:35.064604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum14.29
Range14.29
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1711420442
Coefficient of variation (CV)12.85233116
Kurtosis2868.38205
Mean0.01331603131
Median Absolute Deviation (MAD)0
Skewness42.42376736
Sum469.47
Variance0.02928959928
MonotocityNot monotonic
2021-02-18T22:26:35.170938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033875
96.1%
0.01103
 
0.3%
0.0295
 
0.3%
0.0391
 
0.3%
0.0473
 
0.2%
0.0663
 
0.2%
0.0562
 
0.2%
0.0751
 
0.1%
0.0949
 
0.1%
0.139
 
0.1%
Other values (159)755
 
2.1%
ValueCountFrequency (%)
033875
96.1%
0.01103
 
0.3%
0.0295
 
0.3%
0.0391
 
0.3%
0.0473
 
0.2%
ValueCountFrequency (%)
14.291
< 0.1%
13.891
< 0.1%
6.981
< 0.1%
5.491
< 0.1%
5.361
< 0.1%

Unnamed: 88
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
11
35256 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters70512
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row11
3rd row11
4th row11
5th row11
ValueCountFrequency (%)
1135256
100.0%
2021-02-18T22:26:35.351141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:35.403142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1135256
100.0%

Most occurring characters

ValueCountFrequency (%)
170512
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number70512
100.0%

Most frequent character per category

ValueCountFrequency (%)
170512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common70512
100.0%

Most frequent character per script

ValueCountFrequency (%)
170512
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70512
100.0%

Most frequent character per block

ValueCountFrequency (%)
170512
100.0%

Unnamed: 89
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
LES EUROPÉENS
35256 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters458328
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLES EUROPÉENS
2nd rowLES EUROPÉENS
3rd rowLES EUROPÉENS
4th rowLES EUROPÉENS
5th rowLES EUROPÉENS
ValueCountFrequency (%)
LES EUROPÉENS35256
100.0%
2021-02-18T22:26:35.531739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:35.583386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
les35256
50.0%
européens35256
50.0%

Most occurring characters

ValueCountFrequency (%)
E105768
23.1%
S70512
15.4%
L35256
 
7.7%
35256
 
7.7%
U35256
 
7.7%
R35256
 
7.7%
O35256
 
7.7%
P35256
 
7.7%
É35256
 
7.7%
N35256
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter423072
92.3%
Space Separator35256
 
7.7%

Most frequent character per category

ValueCountFrequency (%)
E105768
25.0%
S70512
16.7%
L35256
 
8.3%
U35256
 
8.3%
R35256
 
8.3%
O35256
 
8.3%
P35256
 
8.3%
É35256
 
8.3%
N35256
 
8.3%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin423072
92.3%
Common35256
 
7.7%

Most frequent character per script

ValueCountFrequency (%)
E105768
25.0%
S70512
16.7%
L35256
 
8.3%
U35256
 
8.3%
R35256
 
8.3%
O35256
 
8.3%
P35256
 
8.3%
É35256
 
8.3%
N35256
 
8.3%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII423072
92.3%
None35256
 
7.7%

Most frequent character per block

ValueCountFrequency (%)
E105768
25.0%
S70512
16.7%
L35256
 
8.3%
35256
 
8.3%
U35256
 
8.3%
R35256
 
8.3%
O35256
 
8.3%
P35256
 
8.3%
N35256
 
8.3%
ValueCountFrequency (%)
É35256
100.0%

Unnamed: 90
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
LES EUROPÉENS
35256 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters458328
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLES EUROPÉENS
2nd rowLES EUROPÉENS
3rd rowLES EUROPÉENS
4th rowLES EUROPÉENS
5th rowLES EUROPÉENS
ValueCountFrequency (%)
LES EUROPÉENS35256
100.0%
2021-02-18T22:26:35.710518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:35.763214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
les35256
50.0%
européens35256
50.0%

Most occurring characters

ValueCountFrequency (%)
E105768
23.1%
S70512
15.4%
L35256
 
7.7%
35256
 
7.7%
U35256
 
7.7%
R35256
 
7.7%
O35256
 
7.7%
P35256
 
7.7%
É35256
 
7.7%
N35256
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter423072
92.3%
Space Separator35256
 
7.7%

Most frequent character per category

ValueCountFrequency (%)
E105768
25.0%
S70512
16.7%
L35256
 
8.3%
U35256
 
8.3%
R35256
 
8.3%
O35256
 
8.3%
P35256
 
8.3%
É35256
 
8.3%
N35256
 
8.3%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin423072
92.3%
Common35256
 
7.7%

Most frequent character per script

ValueCountFrequency (%)
E105768
25.0%
S70512
16.7%
L35256
 
8.3%
U35256
 
8.3%
R35256
 
8.3%
O35256
 
8.3%
P35256
 
8.3%
É35256
 
8.3%
N35256
 
8.3%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII423072
92.3%
None35256
 
7.7%

Most frequent character per block

ValueCountFrequency (%)
E105768
25.0%
S70512
16.7%
L35256
 
8.3%
35256
 
8.3%
U35256
 
8.3%
R35256
 
8.3%
O35256
 
8.3%
P35256
 
8.3%
N35256
 
8.3%
ValueCountFrequency (%)
É35256
100.0%

Unnamed: 91
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
LAGARDE Jean-Christophe
35256 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters810888
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLAGARDE Jean-Christophe
2nd rowLAGARDE Jean-Christophe
3rd rowLAGARDE Jean-Christophe
4th rowLAGARDE Jean-Christophe
5th rowLAGARDE Jean-Christophe
ValueCountFrequency (%)
LAGARDE Jean-Christophe35256
100.0%
2021-02-18T22:26:35.892791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:35.946014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
jean-christophe35256
50.0%
lagarde35256
50.0%

Most occurring characters

ValueCountFrequency (%)
A70512
 
8.7%
e70512
 
8.7%
h70512
 
8.7%
L35256
 
4.3%
G35256
 
4.3%
R35256
 
4.3%
D35256
 
4.3%
E35256
 
4.3%
35256
 
4.3%
J35256
 
4.3%
Other values (10)352560
43.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter423072
52.2%
Uppercase Letter317304
39.1%
Space Separator35256
 
4.3%
Dash Punctuation35256
 
4.3%

Most frequent character per category

ValueCountFrequency (%)
e70512
16.7%
h70512
16.7%
a35256
8.3%
n35256
8.3%
r35256
8.3%
i35256
8.3%
s35256
8.3%
t35256
8.3%
o35256
8.3%
p35256
8.3%
ValueCountFrequency (%)
A70512
22.2%
L35256
11.1%
G35256
11.1%
R35256
11.1%
D35256
11.1%
E35256
11.1%
J35256
11.1%
C35256
11.1%
ValueCountFrequency (%)
35256
100.0%
ValueCountFrequency (%)
-35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin740376
91.3%
Common70512
 
8.7%

Most frequent character per script

ValueCountFrequency (%)
A70512
 
9.5%
e70512
 
9.5%
h70512
 
9.5%
L35256
 
4.8%
G35256
 
4.8%
R35256
 
4.8%
D35256
 
4.8%
E35256
 
4.8%
J35256
 
4.8%
a35256
 
4.8%
Other values (8)282048
38.1%
ValueCountFrequency (%)
35256
50.0%
-35256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII810888
100.0%

Most frequent character per block

ValueCountFrequency (%)
A70512
 
8.7%
e70512
 
8.7%
h70512
 
8.7%
L35256
 
4.3%
G35256
 
4.3%
R35256
 
4.3%
D35256
 
4.3%
E35256
 
4.3%
35256
 
4.3%
J35256
 
4.3%
Other values (10)352560
43.5%

Unnamed: 92
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct423
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.05562174
Minimum0
Maximum12909
Zeros3938
Zeros (%)11.2%
Memory size275.6 KiB
2021-02-18T22:26:36.007933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q312
95-th percentile55
Maximum12909
Range12909
Interquartile range (IQR)10

Descriptive statistics

Standard deviation91.09113945
Coefficient of variation (CV)5.673473188
Kurtosis11619.74014
Mean16.05562174
Median Absolute Deviation (MAD)4
Skewness87.96241213
Sum566057
Variance8297.595686
MonotocityNot monotonic
2021-02-18T22:26:36.119025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03938
 
11.2%
13835
 
10.9%
23601
 
10.2%
33085
 
8.8%
42533
 
7.2%
52048
 
5.8%
61624
 
4.6%
71395
 
4.0%
81188
 
3.4%
91015
 
2.9%
Other values (413)10994
31.2%
ValueCountFrequency (%)
03938
11.2%
13835
10.9%
23601
10.2%
33085
8.8%
42533
7.2%
ValueCountFrequency (%)
129091
< 0.1%
40511
< 0.1%
35201
< 0.1%
30411
< 0.1%
20581
< 0.1%

Unnamed: 93
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct697
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.414293454
Minimum0
Maximum40
Zeros3938
Zeros (%)11.2%
Memory size275.6 KiB
2021-02-18T22:26:36.231568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.73
median1.23
Q31.84
95-th percentile3.39
Maximum40
Range40
Interquartile range (IQR)1.11

Descriptive statistics

Standard deviation1.193878104
Coefficient of variation (CV)0.8441516157
Kurtosis54.27635709
Mean1.414293454
Median Absolute Deviation (MAD)0.55
Skewness3.82720279
Sum49862.33
Variance1.425344927
MonotocityNot monotonic
2021-02-18T22:26:36.343590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03938
 
11.2%
1.16218
 
0.6%
1.32208
 
0.6%
1.28200
 
0.6%
1.08200
 
0.6%
1.23200
 
0.6%
1.1200
 
0.6%
1200
 
0.6%
1.2198
 
0.6%
0.83198
 
0.6%
Other values (687)29496
83.7%
ValueCountFrequency (%)
03938
11.2%
0.041
 
< 0.1%
0.052
 
< 0.1%
0.075
 
< 0.1%
0.085
 
< 0.1%
ValueCountFrequency (%)
401
< 0.1%
26.321
< 0.1%
251
< 0.1%
19.351
< 0.1%
17.241
< 0.1%

Unnamed: 94
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct990
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.649353018
Minimum0
Maximum66.67
Zeros3938
Zeros (%)11.2%
Memory size275.6 KiB
2021-02-18T22:26:36.459457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.42
median2.375
Q33.46
95-th percentile6.17
Maximum66.67
Range66.67
Interquartile range (IQR)2.04

Descriptive statistics

Standard deviation2.09737155
Coefficient of variation (CV)0.79165424
Kurtosis39.66233824
Mean2.649353018
Median Absolute Deviation (MAD)1.015
Skewness3.133757628
Sum93405.59
Variance4.398967419
MonotocityNot monotonic
2021-02-18T22:26:36.564510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03938
 
11.2%
3.13172
 
0.5%
2.7171
 
0.5%
2.22168
 
0.5%
2.44159
 
0.5%
2.33158
 
0.4%
2.56158
 
0.4%
2.5157
 
0.4%
2.94156
 
0.4%
1.85155
 
0.4%
Other values (980)29864
84.7%
ValueCountFrequency (%)
03938
11.2%
0.11
 
< 0.1%
0.151
 
< 0.1%
0.181
 
< 0.1%
0.191
 
< 0.1%
ValueCountFrequency (%)
66.671
< 0.1%
401
< 0.1%
37.51
< 0.1%
28.971
< 0.1%
27.781
< 0.1%

Unnamed: 95
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
12
35256 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters70512
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12
2nd row12
3rd row12
4th row12
5th row12
ValueCountFrequency (%)
1235256
100.0%
2021-02-18T22:26:36.741288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:36.793594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1235256
100.0%

Most occurring characters

ValueCountFrequency (%)
135256
50.0%
235256
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number70512
100.0%

Most frequent character per category

ValueCountFrequency (%)
135256
50.0%
235256
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common70512
100.0%

Most frequent character per script

ValueCountFrequency (%)
135256
50.0%
235256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70512
100.0%

Most frequent character per block

ValueCountFrequency (%)
135256
50.0%
235256
50.0%

Unnamed: 96
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
ENVIE D'EUROPE
35256 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters493584
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowENVIE D'EUROPE
2nd rowENVIE D'EUROPE
3rd rowENVIE D'EUROPE
4th rowENVIE D'EUROPE
5th rowENVIE D'EUROPE
ValueCountFrequency (%)
ENVIE D'EUROPE35256
100.0%
2021-02-18T22:26:36.922520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:36.975429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
d'europe35256
50.0%
envie35256
50.0%

Most occurring characters

ValueCountFrequency (%)
E141024
28.6%
N35256
 
7.1%
V35256
 
7.1%
I35256
 
7.1%
35256
 
7.1%
D35256
 
7.1%
'35256
 
7.1%
U35256
 
7.1%
R35256
 
7.1%
O35256
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter423072
85.7%
Space Separator35256
 
7.1%
Other Punctuation35256
 
7.1%

Most frequent character per category

ValueCountFrequency (%)
E141024
33.3%
N35256
 
8.3%
V35256
 
8.3%
I35256
 
8.3%
D35256
 
8.3%
U35256
 
8.3%
R35256
 
8.3%
O35256
 
8.3%
P35256
 
8.3%
ValueCountFrequency (%)
35256
100.0%
ValueCountFrequency (%)
'35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin423072
85.7%
Common70512
 
14.3%

Most frequent character per script

ValueCountFrequency (%)
E141024
33.3%
N35256
 
8.3%
V35256
 
8.3%
I35256
 
8.3%
D35256
 
8.3%
U35256
 
8.3%
R35256
 
8.3%
O35256
 
8.3%
P35256
 
8.3%
ValueCountFrequency (%)
35256
50.0%
'35256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII493584
100.0%

Most frequent character per block

ValueCountFrequency (%)
E141024
28.6%
N35256
 
7.1%
V35256
 
7.1%
I35256
 
7.1%
35256
 
7.1%
D35256
 
7.1%
'35256
 
7.1%
U35256
 
7.1%
R35256
 
7.1%
O35256
 
7.1%

Unnamed: 97
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
ENVIE D'EUROPE ÉCOLOGIQUE ET SOCIALE
35256 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters1269216
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowENVIE D'EUROPE ÉCOLOGIQUE ET SOCIALE
2nd rowENVIE D'EUROPE ÉCOLOGIQUE ET SOCIALE
3rd rowENVIE D'EUROPE ÉCOLOGIQUE ET SOCIALE
4th rowENVIE D'EUROPE ÉCOLOGIQUE ET SOCIALE
5th rowENVIE D'EUROPE ÉCOLOGIQUE ET SOCIALE
ValueCountFrequency (%)
ENVIE D'EUROPE ÉCOLOGIQUE ET SOCIALE35256
100.0%
2021-02-18T22:26:37.104261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:37.156224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
d'europe35256
20.0%
et35256
20.0%
sociale35256
20.0%
écologique35256
20.0%
envie35256
20.0%

Most occurring characters

ValueCountFrequency (%)
E246792
19.4%
141024
11.1%
O141024
11.1%
I105768
 
8.3%
U70512
 
5.6%
C70512
 
5.6%
L70512
 
5.6%
N35256
 
2.8%
V35256
 
2.8%
D35256
 
2.8%
Other values (9)317304
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1092936
86.1%
Space Separator141024
 
11.1%
Other Punctuation35256
 
2.8%

Most frequent character per category

ValueCountFrequency (%)
E246792
22.6%
O141024
12.9%
I105768
9.7%
U70512
 
6.5%
C70512
 
6.5%
L70512
 
6.5%
N35256
 
3.2%
V35256
 
3.2%
D35256
 
3.2%
R35256
 
3.2%
Other values (7)246792
22.6%
ValueCountFrequency (%)
141024
100.0%
ValueCountFrequency (%)
'35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1092936
86.1%
Common176280
 
13.9%

Most frequent character per script

ValueCountFrequency (%)
E246792
22.6%
O141024
12.9%
I105768
9.7%
U70512
 
6.5%
C70512
 
6.5%
L70512
 
6.5%
N35256
 
3.2%
V35256
 
3.2%
D35256
 
3.2%
R35256
 
3.2%
Other values (7)246792
22.6%
ValueCountFrequency (%)
141024
80.0%
'35256
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1233960
97.2%
None35256
 
2.8%

Most frequent character per block

ValueCountFrequency (%)
E246792
20.0%
141024
11.4%
O141024
11.4%
I105768
 
8.6%
U70512
 
5.7%
C70512
 
5.7%
L70512
 
5.7%
N35256
 
2.9%
V35256
 
2.9%
D35256
 
2.9%
Other values (8)282048
22.9%
ValueCountFrequency (%)
É35256
100.0%

Unnamed: 98
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
GLUCKSMANN Raphaël
35256 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters634608
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGLUCKSMANN Raphaël
2nd rowGLUCKSMANN Raphaël
3rd rowGLUCKSMANN Raphaël
4th rowGLUCKSMANN Raphaël
5th rowGLUCKSMANN Raphaël
ValueCountFrequency (%)
GLUCKSMANN Raphaël35256
100.0%
2021-02-18T22:26:37.285647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:37.338707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
glucksmann35256
50.0%
raphaël35256
50.0%

Most occurring characters

ValueCountFrequency (%)
N70512
 
11.1%
a70512
 
11.1%
G35256
 
5.6%
L35256
 
5.6%
U35256
 
5.6%
C35256
 
5.6%
K35256
 
5.6%
S35256
 
5.6%
M35256
 
5.6%
A35256
 
5.6%
Other values (6)211536
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter387816
61.1%
Lowercase Letter211536
33.3%
Space Separator35256
 
5.6%

Most frequent character per category

ValueCountFrequency (%)
N70512
18.2%
G35256
9.1%
L35256
9.1%
U35256
9.1%
C35256
9.1%
K35256
9.1%
S35256
9.1%
M35256
9.1%
A35256
9.1%
R35256
9.1%
ValueCountFrequency (%)
a70512
33.3%
p35256
16.7%
h35256
16.7%
ë35256
16.7%
l35256
16.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin599352
94.4%
Common35256
 
5.6%

Most frequent character per script

ValueCountFrequency (%)
N70512
 
11.8%
a70512
 
11.8%
G35256
 
5.9%
L35256
 
5.9%
U35256
 
5.9%
C35256
 
5.9%
K35256
 
5.9%
S35256
 
5.9%
M35256
 
5.9%
A35256
 
5.9%
Other values (5)176280
29.4%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII599352
94.4%
None35256
 
5.6%

Most frequent character per block

ValueCountFrequency (%)
N70512
 
11.8%
a70512
 
11.8%
G35256
 
5.9%
L35256
 
5.9%
U35256
 
5.9%
C35256
 
5.9%
K35256
 
5.9%
S35256
 
5.9%
M35256
 
5.9%
A35256
 
5.9%
Other values (5)176280
29.4%
ValueCountFrequency (%)
ë35256
100.0%

Unnamed: 99
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct735
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.79946676
Minimum0
Maximum60814
Zeros1963
Zeros (%)5.6%
Memory size275.6 KiB
2021-02-18T22:26:37.402278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median10
Q325
95-th percentile134
Maximum60814
Range60814
Interquartile range (IQR)21

Descriptive statistics

Standard deviation372.8994791
Coefficient of variation (CV)9.369459178
Kurtosis20105.79738
Mean39.79946676
Median Absolute Deviation (MAD)8
Skewness126.6568727
Sum1403170
Variance139054.0215
MonotocityNot monotonic
2021-02-18T22:26:37.508310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22190
 
6.2%
12111
 
6.0%
32091
 
5.9%
01963
 
5.6%
41922
 
5.5%
51814
 
5.1%
61573
 
4.5%
71413
 
4.0%
81232
 
3.5%
91151
 
3.3%
Other values (725)17796
50.5%
ValueCountFrequency (%)
01963
5.6%
12111
6.0%
22190
6.2%
32091
5.9%
41922
5.5%
ValueCountFrequency (%)
608141
< 0.1%
115321
< 0.1%
113041
< 0.1%
109931
< 0.1%
92221
< 0.1%

Unnamed: 100
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1141
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.923279442
Minimum0
Maximum53.57
Zeros1963
Zeros (%)5.6%
Memory size275.6 KiB
2021-02-18T22:26:37.616591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.66
median2.58
Q33.74
95-th percentile6.42
Maximum53.57
Range53.57
Interquartile range (IQR)2.08

Descriptive statistics

Standard deviation2.166724725
Coefficient of variation (CV)0.74119658
Kurtosis36.32684481
Mean2.923279442
Median Absolute Deviation (MAD)1.02
Skewness3.528580565
Sum103063.14
Variance4.694696033
MonotocityNot monotonic
2021-02-18T22:26:38.296656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01963
 
5.6%
2.5154
 
0.4%
2.7144
 
0.4%
2.44142
 
0.4%
2.56141
 
0.4%
2.86140
 
0.4%
2.22132
 
0.4%
2.17129
 
0.4%
2.42127
 
0.4%
2.41126
 
0.4%
Other values (1131)32058
90.9%
ValueCountFrequency (%)
01963
5.6%
0.021
 
< 0.1%
0.091
 
< 0.1%
0.11
 
< 0.1%
0.111
 
< 0.1%
ValueCountFrequency (%)
53.571
< 0.1%
45.451
< 0.1%
38.151
< 0.1%
37.631
< 0.1%
37.51
< 0.1%

Unnamed: 101
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1682
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.449545609
Minimum0
Maximum83.33
Zeros1963
Zeros (%)5.6%
Memory size275.6 KiB
2021-02-18T22:26:38.402896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.23
median4.98
Q37
95-th percentile11.43
Maximum83.33
Range83.33
Interquartile range (IQR)3.77

Descriptive statistics

Standard deviation3.682047909
Coefficient of variation (CV)0.6756614538
Kurtosis23.15840698
Mean5.449545609
Median Absolute Deviation (MAD)1.86
Skewness2.680546489
Sum192129.18
Variance13.5574768
MonotocityNot monotonic
2021-02-18T22:26:38.502285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01963
 
5.6%
6.25182
 
0.5%
5.26168
 
0.5%
4.17168
 
0.5%
4159
 
0.5%
4.55159
 
0.5%
5158
 
0.4%
5.88152
 
0.4%
6.67146
 
0.4%
4.35140
 
0.4%
Other values (1672)31861
90.4%
ValueCountFrequency (%)
01963
5.6%
0.22
 
< 0.1%
0.291
 
< 0.1%
0.322
 
< 0.1%
0.331
 
< 0.1%
ValueCountFrequency (%)
83.331
< 0.1%
71.431
< 0.1%
601
< 0.1%
55.461
< 0.1%
53.331
< 0.1%

Unnamed: 102
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
13
35256 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters70512
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13
2nd row13
3rd row13
4th row13
5th row13
ValueCountFrequency (%)
1335256
100.0%
2021-02-18T22:26:38.665199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:38.713783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1335256
100.0%

Most occurring characters

ValueCountFrequency (%)
135256
50.0%
335256
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number70512
100.0%

Most frequent character per category

ValueCountFrequency (%)
135256
50.0%
335256
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common70512
100.0%

Most frequent character per script

ValueCountFrequency (%)
135256
50.0%
335256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70512
100.0%

Most frequent character per block

ValueCountFrequency (%)
135256
50.0%
335256
50.0%

Unnamed: 103
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
PARTI FED. EUROPÉEN
35256 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters669864
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPARTI FED. EUROPÉEN
2nd rowPARTI FED. EUROPÉEN
3rd rowPARTI FED. EUROPÉEN
4th rowPARTI FED. EUROPÉEN
5th rowPARTI FED. EUROPÉEN
ValueCountFrequency (%)
PARTI FED. EUROPÉEN35256
100.0%
2021-02-18T22:26:38.833771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:38.883171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
fed35256
33.3%
parti35256
33.3%
européen35256
33.3%

Most occurring characters

ValueCountFrequency (%)
E105768
15.8%
P70512
10.5%
R70512
10.5%
70512
10.5%
A35256
 
5.3%
T35256
 
5.3%
I35256
 
5.3%
F35256
 
5.3%
D35256
 
5.3%
.35256
 
5.3%
Other values (4)141024
21.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter564096
84.2%
Space Separator70512
 
10.5%
Other Punctuation35256
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
E105768
18.8%
P70512
12.5%
R70512
12.5%
A35256
 
6.2%
T35256
 
6.2%
I35256
 
6.2%
F35256
 
6.2%
D35256
 
6.2%
U35256
 
6.2%
O35256
 
6.2%
Other values (2)70512
12.5%
ValueCountFrequency (%)
70512
100.0%
ValueCountFrequency (%)
.35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin564096
84.2%
Common105768
 
15.8%

Most frequent character per script

ValueCountFrequency (%)
E105768
18.8%
P70512
12.5%
R70512
12.5%
A35256
 
6.2%
T35256
 
6.2%
I35256
 
6.2%
F35256
 
6.2%
D35256
 
6.2%
U35256
 
6.2%
O35256
 
6.2%
Other values (2)70512
12.5%
ValueCountFrequency (%)
70512
66.7%
.35256
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII634608
94.7%
None35256
 
5.3%

Most frequent character per block

ValueCountFrequency (%)
E105768
16.7%
P70512
11.1%
R70512
11.1%
70512
11.1%
A35256
 
5.6%
T35256
 
5.6%
I35256
 
5.6%
F35256
 
5.6%
D35256
 
5.6%
.35256
 
5.6%
Other values (3)105768
16.7%
ValueCountFrequency (%)
É35256
100.0%

Unnamed: 104
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
PARTI FÉDÉRALISTE EUROPÉEN - POUR UNE EUROPE QUI PROTÈGE SES CITOYENS
35256 

Length

Max length69
Median length69
Mean length69
Min length69

Characters and Unicode

Total characters2432664
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPARTI FÉDÉRALISTE EUROPÉEN - POUR UNE EUROPE QUI PROTÈGE SES CITOYENS
2nd rowPARTI FÉDÉRALISTE EUROPÉEN - POUR UNE EUROPE QUI PROTÈGE SES CITOYENS
3rd rowPARTI FÉDÉRALISTE EUROPÉEN - POUR UNE EUROPE QUI PROTÈGE SES CITOYENS
4th rowPARTI FÉDÉRALISTE EUROPÉEN - POUR UNE EUROPE QUI PROTÈGE SES CITOYENS
5th rowPARTI FÉDÉRALISTE EUROPÉEN - POUR UNE EUROPE QUI PROTÈGE SES CITOYENS
ValueCountFrequency (%)
PARTI FÉDÉRALISTE EUROPÉEN - POUR UNE EUROPE QUI PROTÈGE SES CITOYENS35256
100.0%
2021-02-18T22:26:39.004753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:39.054366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
ses35256
9.1%
parti35256
9.1%
protège35256
9.1%
europe35256
9.1%
fédéraliste35256
9.1%
citoyens35256
9.1%
pour35256
9.1%
qui35256
9.1%
européen35256
9.1%
une35256
9.1%

Most occurring characters

ValueCountFrequency (%)
352560
14.5%
E317304
13.0%
R211536
8.7%
P176280
 
7.2%
U176280
 
7.2%
O176280
 
7.2%
T141024
 
5.8%
I141024
 
5.8%
S141024
 
5.8%
É105768
 
4.3%
Other values (11)493584
20.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2044848
84.1%
Space Separator352560
 
14.5%
Dash Punctuation35256
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
E317304
15.5%
R211536
10.3%
P176280
8.6%
U176280
8.6%
O176280
8.6%
T141024
 
6.9%
I141024
 
6.9%
S141024
 
6.9%
É105768
 
5.2%
N105768
 
5.2%
Other values (9)352560
17.2%
ValueCountFrequency (%)
352560
100.0%
ValueCountFrequency (%)
-35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2044848
84.1%
Common387816
 
15.9%

Most frequent character per script

ValueCountFrequency (%)
E317304
15.5%
R211536
10.3%
P176280
8.6%
U176280
8.6%
O176280
8.6%
T141024
 
6.9%
I141024
 
6.9%
S141024
 
6.9%
É105768
 
5.2%
N105768
 
5.2%
Other values (9)352560
17.2%
ValueCountFrequency (%)
352560
90.9%
-35256
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2291640
94.2%
None141024
 
5.8%

Most frequent character per block

ValueCountFrequency (%)
352560
15.4%
E317304
13.8%
R211536
9.2%
P176280
7.7%
U176280
7.7%
O176280
7.7%
T141024
 
6.2%
I141024
 
6.2%
S141024
 
6.2%
N105768
 
4.6%
Other values (9)352560
15.4%
ValueCountFrequency (%)
É105768
75.0%
È35256
 
25.0%

Unnamed: 105
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
GERNIGON Yves
35256 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters458328
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGERNIGON Yves
2nd rowGERNIGON Yves
3rd rowGERNIGON Yves
4th rowGERNIGON Yves
5th rowGERNIGON Yves
ValueCountFrequency (%)
GERNIGON Yves35256
100.0%
2021-02-18T22:26:39.177619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:39.227083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
gernigon35256
50.0%
yves35256
50.0%

Most occurring characters

ValueCountFrequency (%)
G70512
15.4%
N70512
15.4%
E35256
7.7%
R35256
7.7%
I35256
7.7%
O35256
7.7%
35256
7.7%
Y35256
7.7%
v35256
7.7%
e35256
7.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter317304
69.2%
Lowercase Letter105768
 
23.1%
Space Separator35256
 
7.7%

Most frequent character per category

ValueCountFrequency (%)
G70512
22.2%
N70512
22.2%
E35256
11.1%
R35256
11.1%
I35256
11.1%
O35256
11.1%
Y35256
11.1%
ValueCountFrequency (%)
v35256
33.3%
e35256
33.3%
s35256
33.3%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin423072
92.3%
Common35256
 
7.7%

Most frequent character per script

ValueCountFrequency (%)
G70512
16.7%
N70512
16.7%
E35256
8.3%
R35256
8.3%
I35256
8.3%
O35256
8.3%
Y35256
8.3%
v35256
8.3%
e35256
8.3%
s35256
8.3%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII458328
100.0%

Most frequent character per block

ValueCountFrequency (%)
G70512
15.4%
N70512
15.4%
E35256
7.7%
R35256
7.7%
I35256
7.7%
O35256
7.7%
35256
7.7%
Y35256
7.7%
v35256
7.7%
e35256
7.7%

Unnamed: 106
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct55
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3445087361
Minimum0
Maximum340
Zeros31209
Zeros (%)88.5%
Memory size275.6 KiB
2021-02-18T22:26:39.285825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum340
Range340
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.94800398
Coefficient of variation (CV)8.557124019
Kurtosis5522.060235
Mean0.3445087361
Median Absolute Deviation (MAD)0
Skewness57.69518679
Sum12146
Variance8.690727468
MonotocityNot monotonic
2021-02-18T22:26:39.386197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
031209
88.5%
12185
 
6.2%
2797
 
2.3%
3348
 
1.0%
4210
 
0.6%
5108
 
0.3%
692
 
0.3%
764
 
0.2%
836
 
0.1%
926
 
0.1%
Other values (45)181
 
0.5%
ValueCountFrequency (%)
031209
88.5%
12185
 
6.2%
2797
 
2.3%
3348
 
1.0%
4210
 
0.6%
ValueCountFrequency (%)
3401
< 0.1%
1641
< 0.1%
1191
< 0.1%
1081
< 0.1%
941
< 0.1%

Unnamed: 107
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct136
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01901832312
Minimum0
Maximum5
Zeros31252
Zeros (%)88.6%
Memory size275.6 KiB
2021-02-18T22:26:39.490313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.11
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1014460927
Coefficient of variation (CV)5.334123941
Kurtosis455.4584156
Mean0.01901832312
Median Absolute Deviation (MAD)0
Skewness16.03422923
Sum670.51
Variance0.01029130972
MonotocityNot monotonic
2021-02-18T22:26:39.593829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
031252
88.6%
0.05285
 
0.8%
0.04270
 
0.8%
0.03263
 
0.7%
0.06257
 
0.7%
0.07208
 
0.6%
0.02205
 
0.6%
0.09197
 
0.6%
0.08192
 
0.5%
0.1167
 
0.5%
Other values (126)1960
 
5.6%
ValueCountFrequency (%)
031252
88.6%
0.01135
 
0.4%
0.02205
 
0.6%
0.03263
 
0.7%
0.04270
 
0.8%
ValueCountFrequency (%)
51
< 0.1%
3.951
< 0.1%
3.451
< 0.1%
3.391
< 0.1%
3.172
< 0.1%

Unnamed: 108
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct197
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03634558657
Minimum0
Maximum9.52
Zeros31213
Zeros (%)88.5%
Memory size275.6 KiB
2021-02-18T22:26:39.694917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.22
Maximum9.52
Range9.52
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1867508968
Coefficient of variation (CV)5.138200109
Kurtosis467.7106807
Mean0.03634558657
Median Absolute Deviation (MAD)0
Skewness15.72477166
Sum1281.4
Variance0.03487589747
MonotocityNot monotonic
2021-02-18T22:26:39.793479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
031213
88.5%
0.09154
 
0.4%
0.08149
 
0.4%
0.12138
 
0.4%
0.1131
 
0.4%
0.07128
 
0.4%
0.06127
 
0.4%
0.11126
 
0.4%
0.14125
 
0.4%
0.05119
 
0.3%
Other values (187)2846
 
8.1%
ValueCountFrequency (%)
031213
88.5%
0.0157
 
0.2%
0.0257
 
0.2%
0.0388
 
0.2%
0.04106
 
0.3%
ValueCountFrequency (%)
9.521
< 0.1%
81
< 0.1%
6.211
< 0.1%
6.061
< 0.1%
5.411
< 0.1%

Unnamed: 109
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
14
35256 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters70512
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row14
2nd row14
3rd row14
4th row14
5th row14
ValueCountFrequency (%)
1435256
100.0%
2021-02-18T22:26:39.953599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:40.001973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1435256
100.0%

Most occurring characters

ValueCountFrequency (%)
135256
50.0%
435256
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number70512
100.0%

Most frequent character per category

ValueCountFrequency (%)
135256
50.0%
435256
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common70512
100.0%

Most frequent character per script

ValueCountFrequency (%)
135256
50.0%
435256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70512
100.0%

Most frequent character per block

ValueCountFrequency (%)
135256
50.0%
435256
50.0%

Unnamed: 110
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
INITIATIVE CITOYENNE
35256 

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters705120
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINITIATIVE CITOYENNE
2nd rowINITIATIVE CITOYENNE
3rd rowINITIATIVE CITOYENNE
4th rowINITIATIVE CITOYENNE
5th rowINITIATIVE CITOYENNE
ValueCountFrequency (%)
INITIATIVE CITOYENNE35256
100.0%
2021-02-18T22:26:40.121721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:40.170899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
initiative35256
50.0%
citoyenne35256
50.0%

Most occurring characters

ValueCountFrequency (%)
I176280
25.0%
N105768
15.0%
T105768
15.0%
E105768
15.0%
A35256
 
5.0%
V35256
 
5.0%
35256
 
5.0%
C35256
 
5.0%
O35256
 
5.0%
Y35256
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter669864
95.0%
Space Separator35256
 
5.0%

Most frequent character per category

ValueCountFrequency (%)
I176280
26.3%
N105768
15.8%
T105768
15.8%
E105768
15.8%
A35256
 
5.3%
V35256
 
5.3%
C35256
 
5.3%
O35256
 
5.3%
Y35256
 
5.3%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin669864
95.0%
Common35256
 
5.0%

Most frequent character per script

ValueCountFrequency (%)
I176280
26.3%
N105768
15.8%
T105768
15.8%
E105768
15.8%
A35256
 
5.3%
V35256
 
5.3%
C35256
 
5.3%
O35256
 
5.3%
Y35256
 
5.3%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII705120
100.0%

Most frequent character per block

ValueCountFrequency (%)
I176280
25.0%
N105768
15.0%
T105768
15.0%
E105768
15.0%
A35256
 
5.0%
V35256
 
5.0%
35256
 
5.0%
C35256
 
5.0%
O35256
 
5.0%
Y35256
 
5.0%

Unnamed: 111
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
MOUVEMENT POUR L'INITIATIVE CITOYENNE
35256 

Length

Max length37
Median length37
Mean length37
Min length37

Characters and Unicode

Total characters1304472
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMOUVEMENT POUR L'INITIATIVE CITOYENNE
2nd rowMOUVEMENT POUR L'INITIATIVE CITOYENNE
3rd rowMOUVEMENT POUR L'INITIATIVE CITOYENNE
4th rowMOUVEMENT POUR L'INITIATIVE CITOYENNE
5th rowMOUVEMENT POUR L'INITIATIVE CITOYENNE
ValueCountFrequency (%)
MOUVEMENT POUR L'INITIATIVE CITOYENNE35256
100.0%
2021-02-18T22:26:40.291625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:40.341191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
mouvement35256
25.0%
citoyenne35256
25.0%
pour35256
25.0%
l'initiative35256
25.0%

Most occurring characters

ValueCountFrequency (%)
E176280
13.5%
I176280
13.5%
N141024
10.8%
T141024
10.8%
O105768
8.1%
105768
8.1%
M70512
 
5.4%
U70512
 
5.4%
V70512
 
5.4%
P35256
 
2.7%
Other values (6)211536
16.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1163448
89.2%
Space Separator105768
 
8.1%
Other Punctuation35256
 
2.7%

Most frequent character per category

ValueCountFrequency (%)
E176280
15.2%
I176280
15.2%
N141024
12.1%
T141024
12.1%
O105768
9.1%
M70512
 
6.1%
U70512
 
6.1%
V70512
 
6.1%
P35256
 
3.0%
R35256
 
3.0%
Other values (4)141024
12.1%
ValueCountFrequency (%)
105768
100.0%
ValueCountFrequency (%)
'35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1163448
89.2%
Common141024
 
10.8%

Most frequent character per script

ValueCountFrequency (%)
E176280
15.2%
I176280
15.2%
N141024
12.1%
T141024
12.1%
O105768
9.1%
M70512
 
6.1%
U70512
 
6.1%
V70512
 
6.1%
P35256
 
3.0%
R35256
 
3.0%
Other values (4)141024
12.1%
ValueCountFrequency (%)
105768
75.0%
'35256
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1304472
100.0%

Most frequent character per block

ValueCountFrequency (%)
E176280
13.5%
I176280
13.5%
N141024
10.8%
T141024
10.8%
O105768
8.1%
105768
8.1%
M70512
 
5.4%
U70512
 
5.4%
V70512
 
5.4%
P35256
 
2.7%
Other values (6)211536
16.2%

Unnamed: 112
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
HELGEN Gilles
35256 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters458328
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHELGEN Gilles
2nd rowHELGEN Gilles
3rd rowHELGEN Gilles
4th rowHELGEN Gilles
5th rowHELGEN Gilles
ValueCountFrequency (%)
HELGEN Gilles35256
100.0%
2021-02-18T22:26:40.462779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:40.511967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
helgen35256
50.0%
gilles35256
50.0%

Most occurring characters

ValueCountFrequency (%)
E70512
15.4%
G70512
15.4%
l70512
15.4%
H35256
7.7%
L35256
7.7%
N35256
7.7%
35256
7.7%
i35256
7.7%
e35256
7.7%
s35256
7.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter246792
53.8%
Lowercase Letter176280
38.5%
Space Separator35256
 
7.7%

Most frequent character per category

ValueCountFrequency (%)
E70512
28.6%
G70512
28.6%
H35256
14.3%
L35256
14.3%
N35256
14.3%
ValueCountFrequency (%)
l70512
40.0%
i35256
20.0%
e35256
20.0%
s35256
20.0%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin423072
92.3%
Common35256
 
7.7%

Most frequent character per script

ValueCountFrequency (%)
E70512
16.7%
G70512
16.7%
l70512
16.7%
H35256
8.3%
L35256
8.3%
N35256
8.3%
i35256
8.3%
e35256
8.3%
s35256
8.3%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII458328
100.0%

Most frequent character per block

ValueCountFrequency (%)
E70512
15.4%
G70512
15.4%
l70512
15.4%
H35256
7.7%
L35256
7.7%
N35256
7.7%
35256
7.7%
i35256
7.7%
e35256
7.7%
s35256
7.7%

Unnamed: 113
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct50
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1668368505
Minimum0
Maximum485
Zeros34102
Zeros (%)96.7%
Memory size275.6 KiB
2021-02-18T22:26:40.568859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum485
Range485
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.35412698
Coefficient of variation (CV)20.10423339
Kurtosis12778.04124
Mean0.1668368505
Median Absolute Deviation (MAD)0
Skewness96.50815185
Sum5882
Variance11.2501678
MonotocityNot monotonic
2021-02-18T22:26:40.675607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034102
96.7%
1499
 
1.4%
2204
 
0.6%
3105
 
0.3%
481
 
0.2%
555
 
0.2%
632
 
0.1%
931
 
0.1%
725
 
0.1%
816
 
< 0.1%
Other values (40)106
 
0.3%
ValueCountFrequency (%)
034102
96.7%
1499
 
1.4%
2204
 
0.6%
3105
 
0.3%
481
 
0.2%
ValueCountFrequency (%)
4851
< 0.1%
1521
< 0.1%
1501
< 0.1%
1401
< 0.1%
1101
< 0.1%

Unnamed: 114
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct127
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.007247560699
Minimum0
Maximum6.35
Zeros34166
Zeros (%)96.9%
Memory size275.6 KiB
2021-02-18T22:26:40.778180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6.35
Range6.35
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.08920524035
Coefficient of variation (CV)12.30831228
Kurtosis1276.121247
Mean0.007247560699
Median Absolute Deviation (MAD)0
Skewness28.95317533
Sum255.52
Variance0.007957574905
MonotocityNot monotonic
2021-02-18T22:26:40.879321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034166
96.9%
0.01125
 
0.4%
0.0291
 
0.3%
0.0380
 
0.2%
0.0472
 
0.2%
0.0546
 
0.1%
0.0738
 
0.1%
0.0636
 
0.1%
0.0830
 
0.1%
0.1230
 
0.1%
Other values (117)542
 
1.5%
ValueCountFrequency (%)
034166
96.9%
0.01125
 
0.4%
0.0291
 
0.3%
0.0380
 
0.2%
0.0472
 
0.2%
ValueCountFrequency (%)
6.351
< 0.1%
4.051
< 0.1%
3.71
< 0.1%
3.511
< 0.1%
3.211
< 0.1%

Unnamed: 115
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct176
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01409632403
Minimum0
Maximum12.12
Zeros34114
Zeros (%)96.8%
Memory size275.6 KiB
2021-02-18T22:26:40.985927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum12.12
Range12.12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1685763096
Coefficient of variation (CV)11.95888441
Kurtosis1266.137236
Mean0.01409632403
Median Absolute Deviation (MAD)0
Skewness28.48818072
Sum496.98
Variance0.02841797217
MonotocityNot monotonic
2021-02-18T22:26:41.098295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034114
96.8%
0.0192
 
0.3%
0.0253
 
0.2%
0.0451
 
0.1%
0.0546
 
0.1%
0.0342
 
0.1%
0.0840
 
0.1%
0.0635
 
0.1%
0.0932
 
0.1%
0.0731
 
0.1%
Other values (166)720
 
2.0%
ValueCountFrequency (%)
034114
96.8%
0.0192
 
0.3%
0.0253
 
0.2%
0.0342
 
0.1%
0.0451
 
0.1%
ValueCountFrequency (%)
12.121
< 0.1%
7.891
< 0.1%
6.351
< 0.1%
6.211
< 0.1%
61
< 0.1%

Unnamed: 116
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
15
35256 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters70512
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15
2nd row15
3rd row15
4th row15
5th row15
ValueCountFrequency (%)
1535256
100.0%
2021-02-18T22:26:41.273041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:41.324959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1535256
100.0%

Most occurring characters

ValueCountFrequency (%)
135256
50.0%
535256
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number70512
100.0%

Most frequent character per category

ValueCountFrequency (%)
135256
50.0%
535256
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common70512
100.0%

Most frequent character per script

ValueCountFrequency (%)
135256
50.0%
535256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70512
100.0%

Most frequent character per block

ValueCountFrequency (%)
135256
50.0%
535256
50.0%

Unnamed: 117
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
DEBOUT LA FRANCE
35256 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters564096
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDEBOUT LA FRANCE
2nd rowDEBOUT LA FRANCE
3rd rowDEBOUT LA FRANCE
4th rowDEBOUT LA FRANCE
5th rowDEBOUT LA FRANCE
ValueCountFrequency (%)
DEBOUT LA FRANCE35256
100.0%
2021-02-18T22:26:41.453400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:41.506444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
debout35256
33.3%
la35256
33.3%
france35256
33.3%

Most occurring characters

ValueCountFrequency (%)
E70512
12.5%
70512
12.5%
A70512
12.5%
D35256
 
6.2%
B35256
 
6.2%
O35256
 
6.2%
U35256
 
6.2%
T35256
 
6.2%
L35256
 
6.2%
F35256
 
6.2%
Other values (3)105768
18.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter493584
87.5%
Space Separator70512
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
E70512
14.3%
A70512
14.3%
D35256
7.1%
B35256
7.1%
O35256
7.1%
U35256
7.1%
T35256
7.1%
L35256
7.1%
F35256
7.1%
R35256
7.1%
Other values (2)70512
14.3%
ValueCountFrequency (%)
70512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin493584
87.5%
Common70512
 
12.5%

Most frequent character per script

ValueCountFrequency (%)
E70512
14.3%
A70512
14.3%
D35256
7.1%
B35256
7.1%
O35256
7.1%
U35256
7.1%
T35256
7.1%
L35256
7.1%
F35256
7.1%
R35256
7.1%
Other values (2)70512
14.3%
ValueCountFrequency (%)
70512
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII564096
100.0%

Most frequent character per block

ValueCountFrequency (%)
E70512
12.5%
70512
12.5%
A70512
12.5%
D35256
 
6.2%
B35256
 
6.2%
O35256
 
6.2%
U35256
 
6.2%
T35256
 
6.2%
L35256
 
6.2%
F35256
 
6.2%
Other values (3)105768
18.8%

Unnamed: 118
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
LE COURAGE DE DÉFENDRE LES FRANÇAIS AVEC NICOLAS DUPONT-AIGNAN. DEBOUT LA FRANCE ! - CNIP
35256 

Length

Max length89
Median length89
Mean length89
Min length89

Characters and Unicode

Total characters3137784
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLE COURAGE DE DÉFENDRE LES FRANÇAIS AVEC NICOLAS DUPONT-AIGNAN. DEBOUT LA FRANCE ! - CNIP
2nd rowLE COURAGE DE DÉFENDRE LES FRANÇAIS AVEC NICOLAS DUPONT-AIGNAN. DEBOUT LA FRANCE ! - CNIP
3rd rowLE COURAGE DE DÉFENDRE LES FRANÇAIS AVEC NICOLAS DUPONT-AIGNAN. DEBOUT LA FRANCE ! - CNIP
4th rowLE COURAGE DE DÉFENDRE LES FRANÇAIS AVEC NICOLAS DUPONT-AIGNAN. DEBOUT LA FRANCE ! - CNIP
5th rowLE COURAGE DE DÉFENDRE LES FRANÇAIS AVEC NICOLAS DUPONT-AIGNAN. DEBOUT LA FRANCE ! - CNIP
ValueCountFrequency (%)
LE COURAGE DE DÉFENDRE LES FRANÇAIS AVEC NICOLAS DUPONT-AIGNAN. DEBOUT LA FRANCE ! - CNIP35256
100.0%
2021-02-18T22:26:41.636249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:41.689140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
70512
13.3%
de35256
 
6.7%
défendre35256
 
6.7%
avec35256
 
6.7%
france35256
 
6.7%
dupont-aignan35256
 
6.7%
nicolas35256
 
6.7%
les35256
 
6.7%
la35256
 
6.7%
debout35256
 
6.7%
Other values (4)141024
26.7%

Most occurring characters

ValueCountFrequency (%)
493584
15.7%
E317304
 
10.1%
A317304
 
10.1%
N282048
 
9.0%
C176280
 
5.6%
D176280
 
5.6%
L141024
 
4.5%
O141024
 
4.5%
R141024
 
4.5%
I141024
 
4.5%
Other values (13)810888
25.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2503176
79.8%
Space Separator493584
 
15.7%
Dash Punctuation70512
 
2.2%
Other Punctuation70512
 
2.2%

Most frequent character per category

ValueCountFrequency (%)
E317304
12.7%
A317304
12.7%
N282048
11.3%
C176280
 
7.0%
D176280
 
7.0%
L141024
 
5.6%
O141024
 
5.6%
R141024
 
5.6%
I141024
 
5.6%
U105768
 
4.2%
Other values (9)564096
22.5%
ValueCountFrequency (%)
.35256
50.0%
!35256
50.0%
ValueCountFrequency (%)
493584
100.0%
ValueCountFrequency (%)
-70512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2503176
79.8%
Common634608
 
20.2%

Most frequent character per script

ValueCountFrequency (%)
E317304
12.7%
A317304
12.7%
N282048
11.3%
C176280
 
7.0%
D176280
 
7.0%
L141024
 
5.6%
O141024
 
5.6%
R141024
 
5.6%
I141024
 
5.6%
U105768
 
4.2%
Other values (9)564096
22.5%
ValueCountFrequency (%)
493584
77.8%
-70512
 
11.1%
.35256
 
5.6%
!35256
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII3067272
97.8%
None70512
 
2.2%

Most frequent character per block

ValueCountFrequency (%)
493584
16.1%
E317304
10.3%
A317304
10.3%
N282048
 
9.2%
C176280
 
5.7%
D176280
 
5.7%
L141024
 
4.6%
O141024
 
4.6%
R141024
 
4.6%
I141024
 
4.6%
Other values (11)740376
24.1%
ValueCountFrequency (%)
É35256
50.0%
Ç35256
50.0%

Unnamed: 119
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
DUPONT-AIGNAN Nicolas
35256 

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters740376
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDUPONT-AIGNAN Nicolas
2nd rowDUPONT-AIGNAN Nicolas
3rd rowDUPONT-AIGNAN Nicolas
4th rowDUPONT-AIGNAN Nicolas
5th rowDUPONT-AIGNAN Nicolas
ValueCountFrequency (%)
DUPONT-AIGNAN Nicolas35256
100.0%
2021-02-18T22:26:41.820639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:41.873911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
nicolas35256
50.0%
dupont-aignan35256
50.0%

Most occurring characters

ValueCountFrequency (%)
N141024
19.0%
A70512
 
9.5%
D35256
 
4.8%
U35256
 
4.8%
P35256
 
4.8%
O35256
 
4.8%
T35256
 
4.8%
-35256
 
4.8%
I35256
 
4.8%
G35256
 
4.8%
Other values (7)246792
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter458328
61.9%
Lowercase Letter211536
28.6%
Dash Punctuation35256
 
4.8%
Space Separator35256
 
4.8%

Most frequent character per category

ValueCountFrequency (%)
N141024
30.8%
A70512
15.4%
D35256
 
7.7%
U35256
 
7.7%
P35256
 
7.7%
O35256
 
7.7%
T35256
 
7.7%
I35256
 
7.7%
G35256
 
7.7%
ValueCountFrequency (%)
i35256
16.7%
c35256
16.7%
o35256
16.7%
l35256
16.7%
a35256
16.7%
s35256
16.7%
ValueCountFrequency (%)
-35256
100.0%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin669864
90.5%
Common70512
 
9.5%

Most frequent character per script

ValueCountFrequency (%)
N141024
21.1%
A70512
 
10.5%
D35256
 
5.3%
U35256
 
5.3%
P35256
 
5.3%
O35256
 
5.3%
T35256
 
5.3%
I35256
 
5.3%
G35256
 
5.3%
i35256
 
5.3%
Other values (5)176280
26.3%
ValueCountFrequency (%)
-35256
50.0%
35256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII740376
100.0%

Most frequent character per block

ValueCountFrequency (%)
N141024
19.0%
A70512
 
9.5%
D35256
 
4.8%
U35256
 
4.8%
P35256
 
4.8%
O35256
 
4.8%
T35256
 
4.8%
-35256
 
4.8%
I35256
 
4.8%
G35256
 
4.8%
Other values (7)246792
33.3%

Unnamed: 120
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct459
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.5637622
Minimum0
Maximum9427
Zeros1802
Zeros (%)5.1%
Memory size275.6 KiB
2021-02-18T22:26:41.937658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median9
Q320
95-th percentile81
Maximum9427
Range9427
Interquartile range (IQR)16

Descriptive statistics

Standard deviation81.64507118
Coefficient of variation (CV)3.618415691
Kurtosis5560.612952
Mean22.5637622
Median Absolute Deviation (MAD)7
Skewness56.10792269
Sum795508
Variance6665.917647
MonotocityNot monotonic
2021-02-18T22:26:42.044810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22404
 
6.8%
32263
 
6.4%
12167
 
6.1%
42165
 
6.1%
51977
 
5.6%
01802
 
5.1%
61778
 
5.0%
71588
 
4.5%
81392
 
3.9%
91251
 
3.5%
Other values (449)16469
46.7%
ValueCountFrequency (%)
01802
5.1%
12167
6.1%
22404
6.8%
32263
6.4%
42165
6.1%
ValueCountFrequency (%)
94271
< 0.1%
51611
< 0.1%
30211
< 0.1%
23681
< 0.1%
23301
< 0.1%

Unnamed: 121
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct929
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.43328398
Minimum0
Maximum55.56
Zeros1802
Zeros (%)5.1%
Memory size275.6 KiB
2021-02-18T22:26:42.150928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.46
median2.16
Q33.07
95-th percentile5.26
Maximum55.56
Range55.56
Interquartile range (IQR)1.61

Descriptive statistics

Standard deviation1.67606844
Coefficient of variation (CV)0.6888092201
Kurtosis49.09979289
Mean2.43328398
Median Absolute Deviation (MAD)0.78
Skewness3.388226755
Sum85787.86
Variance2.809205417
MonotocityNot monotonic
2021-02-18T22:26:42.250916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01802
 
5.1%
1.96168
 
0.5%
1.92164
 
0.5%
2.22158
 
0.4%
1.67158
 
0.4%
1.69158
 
0.4%
1.75156
 
0.4%
1.82155
 
0.4%
1.72153
 
0.4%
1.64153
 
0.4%
Other values (919)32031
90.9%
ValueCountFrequency (%)
01802
5.1%
0.043
 
< 0.1%
0.061
 
< 0.1%
0.075
 
< 0.1%
0.084
 
< 0.1%
ValueCountFrequency (%)
55.561
< 0.1%
40.581
< 0.1%
30.951
< 0.1%
301
< 0.1%
22.861
< 0.1%

Unnamed: 122
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1337
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.539032789
Minimum0
Maximum72.22
Zeros1802
Zeros (%)5.1%
Memory size275.6 KiB
2021-02-18T22:26:42.358145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.8
median4.11
Q35.77
95-th percentile9.52
Maximum72.22
Range72.22
Interquartile range (IQR)2.97

Descriptive statistics

Standard deviation2.894472968
Coefficient of variation (CV)0.6376849657
Kurtosis22.17862246
Mean4.539032789
Median Absolute Deviation (MAD)1.45
Skewness2.381681332
Sum160028.14
Variance8.377973763
MonotocityNot monotonic
2021-02-18T22:26:42.463794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01802
 
5.1%
3.85192
 
0.5%
5179
 
0.5%
6.25178
 
0.5%
5.56173
 
0.5%
4.55169
 
0.5%
5.88165
 
0.5%
4.17164
 
0.5%
4164
 
0.5%
5.26162
 
0.5%
Other values (1327)31908
90.5%
ValueCountFrequency (%)
01802
5.1%
0.261
 
< 0.1%
0.311
 
< 0.1%
0.322
 
< 0.1%
0.332
 
< 0.1%
ValueCountFrequency (%)
72.221
< 0.1%
58.331
< 0.1%
55.561
< 0.1%
39.131
< 0.1%
38.461
< 0.1%

Unnamed: 123
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
16
35256 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters70512
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row16
2nd row16
3rd row16
4th row16
5th row16
ValueCountFrequency (%)
1635256
100.0%
2021-02-18T22:26:42.641777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:42.693954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1635256
100.0%

Most occurring characters

ValueCountFrequency (%)
135256
50.0%
635256
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number70512
100.0%

Most frequent character per category

ValueCountFrequency (%)
135256
50.0%
635256
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common70512
100.0%

Most frequent character per script

ValueCountFrequency (%)
135256
50.0%
635256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70512
100.0%

Most frequent character per block

ValueCountFrequency (%)
135256
50.0%
635256
50.0%

Unnamed: 124
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
ALLONS ENFANTS
35256 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters493584
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowALLONS ENFANTS
2nd rowALLONS ENFANTS
3rd rowALLONS ENFANTS
4th rowALLONS ENFANTS
5th rowALLONS ENFANTS
ValueCountFrequency (%)
ALLONS ENFANTS35256
100.0%
2021-02-18T22:26:42.821260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:42.872732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
allons35256
50.0%
enfants35256
50.0%

Most occurring characters

ValueCountFrequency (%)
N105768
21.4%
A70512
14.3%
L70512
14.3%
S70512
14.3%
O35256
 
7.1%
35256
 
7.1%
E35256
 
7.1%
F35256
 
7.1%
T35256
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter458328
92.9%
Space Separator35256
 
7.1%

Most frequent character per category

ValueCountFrequency (%)
N105768
23.1%
A70512
15.4%
L70512
15.4%
S70512
15.4%
O35256
 
7.7%
E35256
 
7.7%
F35256
 
7.7%
T35256
 
7.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin458328
92.9%
Common35256
 
7.1%

Most frequent character per script

ValueCountFrequency (%)
N105768
23.1%
A70512
15.4%
L70512
15.4%
S70512
15.4%
O35256
 
7.7%
E35256
 
7.7%
F35256
 
7.7%
T35256
 
7.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII493584
100.0%

Most frequent character per block

ValueCountFrequency (%)
N105768
21.4%
A70512
14.3%
L70512
14.3%
S70512
14.3%
O35256
 
7.1%
35256
 
7.1%
E35256
 
7.1%
F35256
 
7.1%
T35256
 
7.1%

Unnamed: 125
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
ALLONS ENFANTS
35256 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters493584
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowALLONS ENFANTS
2nd rowALLONS ENFANTS
3rd rowALLONS ENFANTS
4th rowALLONS ENFANTS
5th rowALLONS ENFANTS
ValueCountFrequency (%)
ALLONS ENFANTS35256
100.0%
2021-02-18T22:26:43.001520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:43.054131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
allons35256
50.0%
enfants35256
50.0%

Most occurring characters

ValueCountFrequency (%)
N105768
21.4%
A70512
14.3%
L70512
14.3%
S70512
14.3%
O35256
 
7.1%
35256
 
7.1%
E35256
 
7.1%
F35256
 
7.1%
T35256
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter458328
92.9%
Space Separator35256
 
7.1%

Most frequent character per category

ValueCountFrequency (%)
N105768
23.1%
A70512
15.4%
L70512
15.4%
S70512
15.4%
O35256
 
7.7%
E35256
 
7.7%
F35256
 
7.7%
T35256
 
7.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin458328
92.9%
Common35256
 
7.1%

Most frequent character per script

ValueCountFrequency (%)
N105768
23.1%
A70512
15.4%
L70512
15.4%
S70512
15.4%
O35256
 
7.7%
E35256
 
7.7%
F35256
 
7.7%
T35256
 
7.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII493584
100.0%

Most frequent character per block

ValueCountFrequency (%)
N105768
21.4%
A70512
14.3%
L70512
14.3%
S70512
14.3%
O35256
 
7.1%
35256
 
7.1%
E35256
 
7.1%
F35256
 
7.1%
T35256
 
7.1%

Unnamed: 126
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
CAILLAUD Sophie
35256 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters528840
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCAILLAUD Sophie
2nd rowCAILLAUD Sophie
3rd rowCAILLAUD Sophie
4th rowCAILLAUD Sophie
5th rowCAILLAUD Sophie
ValueCountFrequency (%)
CAILLAUD Sophie35256
100.0%
2021-02-18T22:26:43.183736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:43.237246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
sophie35256
50.0%
caillaud35256
50.0%

Most occurring characters

ValueCountFrequency (%)
A70512
13.3%
L70512
13.3%
C35256
 
6.7%
I35256
 
6.7%
U35256
 
6.7%
D35256
 
6.7%
35256
 
6.7%
S35256
 
6.7%
o35256
 
6.7%
p35256
 
6.7%
Other values (3)105768
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter317304
60.0%
Lowercase Letter176280
33.3%
Space Separator35256
 
6.7%

Most frequent character per category

ValueCountFrequency (%)
A70512
22.2%
L70512
22.2%
C35256
11.1%
I35256
11.1%
U35256
11.1%
D35256
11.1%
S35256
11.1%
ValueCountFrequency (%)
o35256
20.0%
p35256
20.0%
h35256
20.0%
i35256
20.0%
e35256
20.0%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin493584
93.3%
Common35256
 
6.7%

Most frequent character per script

ValueCountFrequency (%)
A70512
14.3%
L70512
14.3%
C35256
7.1%
I35256
7.1%
U35256
7.1%
D35256
7.1%
S35256
7.1%
o35256
7.1%
p35256
7.1%
h35256
7.1%
Other values (2)70512
14.3%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII528840
100.0%

Most frequent character per block

ValueCountFrequency (%)
A70512
13.3%
L70512
13.3%
C35256
 
6.7%
I35256
 
6.7%
U35256
 
6.7%
D35256
 
6.7%
35256
 
6.7%
S35256
 
6.7%
o35256
 
6.7%
p35256
 
6.7%
Other values (3)105768
20.0%

Unnamed: 127
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct40
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2286702973
Minimum0
Maximum800
Zeros32282
Zeros (%)91.6%
Memory size275.6 KiB
2021-02-18T22:26:43.297565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum800
Range800
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.67116509
Coefficient of variation (CV)20.42751134
Kurtosis24464.64961
Mean0.2286702973
Median Absolute Deviation (MAD)0
Skewness145.572202
Sum8062
Variance21.81978329
MonotocityNot monotonic
2021-02-18T22:26:43.403843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
032282
91.6%
11678
 
4.8%
2694
 
2.0%
3283
 
0.8%
4112
 
0.3%
546
 
0.1%
633
 
0.1%
720
 
0.1%
917
 
< 0.1%
813
 
< 0.1%
Other values (30)78
 
0.2%
ValueCountFrequency (%)
032282
91.6%
11678
 
4.8%
2694
 
2.0%
3283
 
0.8%
4112
 
0.3%
ValueCountFrequency (%)
8001
< 0.1%
1711
< 0.1%
1421
< 0.1%
851
< 0.1%
791
< 0.1%

Unnamed: 128
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct150
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01484711822
Minimum0
Maximum9.52
Zeros32317
Zeros (%)91.7%
Memory size275.6 KiB
2021-02-18T22:26:43.526431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.05
Maximum9.52
Range9.52
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1227001072
Coefficient of variation (CV)8.264237229
Kurtosis1659.164337
Mean0.01484711822
Median Absolute Deviation (MAD)0
Skewness30.82745031
Sum523.45
Variance0.01505531629
MonotocityNot monotonic
2021-02-18T22:26:43.651412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
032317
91.7%
0.01361
 
1.0%
0.02311
 
0.9%
0.03224
 
0.6%
0.04202
 
0.6%
0.05174
 
0.5%
0.06140
 
0.4%
0.07120
 
0.3%
0.08116
 
0.3%
0.181
 
0.2%
Other values (140)1210
 
3.4%
ValueCountFrequency (%)
032317
91.7%
0.01361
 
1.0%
0.02311
 
0.9%
0.03224
 
0.6%
0.04202
 
0.6%
ValueCountFrequency (%)
9.521
< 0.1%
7.141
< 0.1%
5.621
< 0.1%
3.981
< 0.1%
3.851
< 0.1%

Unnamed: 129
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct203
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02737151123
Minimum0
Maximum11.63
Zeros32286
Zeros (%)91.6%
Memory size275.6 KiB
2021-02-18T22:26:43.768270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.1
Maximum11.63
Range11.63
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2043851635
Coefficient of variation (CV)7.467076327
Kurtosis854.7874806
Mean0.02737151123
Median Absolute Deviation (MAD)0
Skewness22.69847299
Sum965.01
Variance0.04177329508
MonotocityNot monotonic
2021-02-18T22:26:43.886848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
032286
91.6%
0.03215
 
0.6%
0.02182
 
0.5%
0.04151
 
0.4%
0.05136
 
0.4%
0.07115
 
0.3%
0.08112
 
0.3%
0.09111
 
0.3%
0.06108
 
0.3%
0.1280
 
0.2%
Other values (193)1760
 
5.0%
ValueCountFrequency (%)
032286
91.6%
0.0165
 
0.2%
0.02182
 
0.5%
0.03215
 
0.6%
0.04151
 
0.4%
ValueCountFrequency (%)
11.631
< 0.1%
11.111
< 0.1%
8.331
< 0.1%
7.51
< 0.1%
7.161
< 0.1%

Unnamed: 130
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
17
35256 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters70512
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row17
2nd row17
3rd row17
4th row17
5th row17
ValueCountFrequency (%)
1735256
100.0%
2021-02-18T22:26:44.069651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:44.122993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1735256
100.0%

Most occurring characters

ValueCountFrequency (%)
135256
50.0%
735256
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number70512
100.0%

Most frequent character per category

ValueCountFrequency (%)
135256
50.0%
735256
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common70512
100.0%

Most frequent character per script

ValueCountFrequency (%)
135256
50.0%
735256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70512
100.0%

Most frequent character per block

ValueCountFrequency (%)
135256
50.0%
735256
50.0%

Unnamed: 131
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
DÉCROISSANCE 2019
35256 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters599352
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDÉCROISSANCE 2019
2nd rowDÉCROISSANCE 2019
3rd rowDÉCROISSANCE 2019
4th rowDÉCROISSANCE 2019
5th rowDÉCROISSANCE 2019
ValueCountFrequency (%)
DÉCROISSANCE 201935256
100.0%
2021-02-18T22:26:44.252734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:44.305560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
201935256
50.0%
décroissance35256
50.0%

Most occurring characters

ValueCountFrequency (%)
C70512
 
11.8%
S70512
 
11.8%
D35256
 
5.9%
É35256
 
5.9%
R35256
 
5.9%
O35256
 
5.9%
I35256
 
5.9%
A35256
 
5.9%
N35256
 
5.9%
E35256
 
5.9%
Other values (5)176280
29.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter423072
70.6%
Decimal Number141024
 
23.5%
Space Separator35256
 
5.9%

Most frequent character per category

ValueCountFrequency (%)
C70512
16.7%
S70512
16.7%
D35256
8.3%
É35256
8.3%
R35256
8.3%
O35256
8.3%
I35256
8.3%
A35256
8.3%
N35256
8.3%
E35256
8.3%
ValueCountFrequency (%)
235256
25.0%
035256
25.0%
135256
25.0%
935256
25.0%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin423072
70.6%
Common176280
29.4%

Most frequent character per script

ValueCountFrequency (%)
C70512
16.7%
S70512
16.7%
D35256
8.3%
É35256
8.3%
R35256
8.3%
O35256
8.3%
I35256
8.3%
A35256
8.3%
N35256
8.3%
E35256
8.3%
ValueCountFrequency (%)
35256
20.0%
235256
20.0%
035256
20.0%
135256
20.0%
935256
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII564096
94.1%
None35256
 
5.9%

Most frequent character per block

ValueCountFrequency (%)
C70512
12.5%
S70512
12.5%
D35256
 
6.2%
R35256
 
6.2%
O35256
 
6.2%
I35256
 
6.2%
A35256
 
6.2%
N35256
 
6.2%
E35256
 
6.2%
35256
 
6.2%
Other values (4)141024
25.0%
ValueCountFrequency (%)
É35256
100.0%

Unnamed: 132
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
DÉCROISSANCE 2019
35256 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters599352
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDÉCROISSANCE 2019
2nd rowDÉCROISSANCE 2019
3rd rowDÉCROISSANCE 2019
4th rowDÉCROISSANCE 2019
5th rowDÉCROISSANCE 2019
ValueCountFrequency (%)
DÉCROISSANCE 201935256
100.0%
2021-02-18T22:26:44.435237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:44.489462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
201935256
50.0%
décroissance35256
50.0%

Most occurring characters

ValueCountFrequency (%)
C70512
 
11.8%
S70512
 
11.8%
D35256
 
5.9%
É35256
 
5.9%
R35256
 
5.9%
O35256
 
5.9%
I35256
 
5.9%
A35256
 
5.9%
N35256
 
5.9%
E35256
 
5.9%
Other values (5)176280
29.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter423072
70.6%
Decimal Number141024
 
23.5%
Space Separator35256
 
5.9%

Most frequent character per category

ValueCountFrequency (%)
C70512
16.7%
S70512
16.7%
D35256
8.3%
É35256
8.3%
R35256
8.3%
O35256
8.3%
I35256
8.3%
A35256
8.3%
N35256
8.3%
E35256
8.3%
ValueCountFrequency (%)
235256
25.0%
035256
25.0%
135256
25.0%
935256
25.0%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin423072
70.6%
Common176280
29.4%

Most frequent character per script

ValueCountFrequency (%)
C70512
16.7%
S70512
16.7%
D35256
8.3%
É35256
8.3%
R35256
8.3%
O35256
8.3%
I35256
8.3%
A35256
8.3%
N35256
8.3%
E35256
8.3%
ValueCountFrequency (%)
35256
20.0%
235256
20.0%
035256
20.0%
135256
20.0%
935256
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII564096
94.1%
None35256
 
5.9%

Most frequent character per block

ValueCountFrequency (%)
C70512
12.5%
S70512
12.5%
D35256
 
6.2%
R35256
 
6.2%
O35256
 
6.2%
I35256
 
6.2%
A35256
 
6.2%
N35256
 
6.2%
E35256
 
6.2%
35256
 
6.2%
Other values (4)141024
25.0%
ValueCountFrequency (%)
É35256
100.0%

Unnamed: 133
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
DELFEL Thérèse
35256 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters493584
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDELFEL Thérèse
2nd rowDELFEL Thérèse
3rd rowDELFEL Thérèse
4th rowDELFEL Thérèse
5th rowDELFEL Thérèse
ValueCountFrequency (%)
DELFEL Thérèse35256
100.0%
2021-02-18T22:26:44.639206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:44.713499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
thérèse35256
50.0%
delfel35256
50.0%

Most occurring characters

ValueCountFrequency (%)
E70512
14.3%
L70512
14.3%
D35256
7.1%
F35256
7.1%
35256
7.1%
T35256
7.1%
h35256
7.1%
é35256
7.1%
r35256
7.1%
è35256
7.1%
Other values (2)70512
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter246792
50.0%
Lowercase Letter211536
42.9%
Space Separator35256
 
7.1%

Most frequent character per category

ValueCountFrequency (%)
h35256
16.7%
é35256
16.7%
r35256
16.7%
è35256
16.7%
s35256
16.7%
e35256
16.7%
ValueCountFrequency (%)
E70512
28.6%
L70512
28.6%
D35256
14.3%
F35256
14.3%
T35256
14.3%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin458328
92.9%
Common35256
 
7.1%

Most frequent character per script

ValueCountFrequency (%)
E70512
15.4%
L70512
15.4%
D35256
7.7%
F35256
7.7%
T35256
7.7%
h35256
7.7%
é35256
7.7%
r35256
7.7%
è35256
7.7%
s35256
7.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII423072
85.7%
None70512
 
14.3%

Most frequent character per block

ValueCountFrequency (%)
E70512
16.7%
L70512
16.7%
D35256
8.3%
F35256
8.3%
35256
8.3%
T35256
8.3%
h35256
8.3%
r35256
8.3%
s35256
8.3%
e35256
8.3%
ValueCountFrequency (%)
é35256
50.0%
è35256
50.0%

Unnamed: 134
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2936237803
Minimum0
Maximum271
Zeros30724
Zeros (%)87.1%
Memory size275.6 KiB
2021-02-18T22:26:44.795381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum271
Range271
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.138240453
Coefficient of variation (CV)7.282245499
Kurtosis7583.90139
Mean0.2936237803
Median Absolute Deviation (MAD)0
Skewness67.67045589
Sum10352
Variance4.572072234
MonotocityNot monotonic
2021-02-18T22:26:44.935133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
030724
87.1%
12544
 
7.2%
21077
 
3.1%
3400
 
1.1%
4184
 
0.5%
5100
 
0.3%
658
 
0.2%
732
 
0.1%
924
 
0.1%
821
 
0.1%
Other values (32)92
 
0.3%
ValueCountFrequency (%)
030724
87.1%
12544
 
7.2%
21077
 
3.1%
3400
 
1.1%
4184
 
0.5%
ValueCountFrequency (%)
2711
< 0.1%
931
< 0.1%
791
< 0.1%
681
< 0.1%
671
< 0.1%

Unnamed: 135
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct205
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03206461312
Minimum0
Maximum10
Zeros30760
Zeros (%)87.2%
Memory size275.6 KiB
2021-02-18T22:26:45.059178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.15
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1937333368
Coefficient of variation (CV)6.041967077
Kurtosis493.4448541
Mean0.03206461312
Median Absolute Deviation (MAD)0
Skewness17.10221188
Sum1130.47
Variance0.03753260578
MonotocityNot monotonic
2021-02-18T22:26:45.172504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
030760
87.2%
0.02388
 
1.1%
0.03343
 
1.0%
0.01302
 
0.9%
0.04295
 
0.8%
0.05245
 
0.7%
0.07181
 
0.5%
0.06181
 
0.5%
0.08146
 
0.4%
0.09145
 
0.4%
Other values (195)2270
 
6.4%
ValueCountFrequency (%)
030760
87.2%
0.01302
 
0.9%
0.02388
 
1.1%
0.03343
 
1.0%
0.04295
 
0.8%
ValueCountFrequency (%)
101
< 0.1%
7.411
< 0.1%
6.821
< 0.1%
6.251
< 0.1%
5.561
< 0.1%

Unnamed: 136
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct269
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05877808033
Minimum0
Maximum14.29
Zeros30727
Zeros (%)87.2%
Memory size275.6 KiB
2021-02-18T22:26:46.015324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.28
Maximum14.29
Range14.29
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3376756443
Coefficient of variation (CV)5.744924679
Kurtosis391.8118771
Mean0.05877808033
Median Absolute Deviation (MAD)0
Skewness15.53059211
Sum2072.28
Variance0.1140248407
MonotocityNot monotonic
2021-02-18T22:26:46.126873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
030727
87.2%
0.04211
 
0.6%
0.05196
 
0.6%
0.03189
 
0.5%
0.07178
 
0.5%
0.06176
 
0.5%
0.09145
 
0.4%
0.08144
 
0.4%
0.02130
 
0.4%
0.1121
 
0.3%
Other values (259)3039
 
8.6%
ValueCountFrequency (%)
030727
87.2%
0.0153
 
0.2%
0.02130
 
0.4%
0.03189
 
0.5%
0.04211
 
0.6%
ValueCountFrequency (%)
14.291
< 0.1%
13.331
< 0.1%
11.321
< 0.1%
10.711
< 0.1%
10.261
< 0.1%

Unnamed: 137
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
18
35256 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters70512
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row18
2nd row18
3rd row18
4th row18
5th row18
ValueCountFrequency (%)
1835256
100.0%
2021-02-18T22:26:46.295703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:46.344408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1835256
100.0%

Most occurring characters

ValueCountFrequency (%)
135256
50.0%
835256
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number70512
100.0%

Most frequent character per category

ValueCountFrequency (%)
135256
50.0%
835256
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common70512
100.0%

Most frequent character per script

ValueCountFrequency (%)
135256
50.0%
835256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70512
100.0%

Most frequent character per block

ValueCountFrequency (%)
135256
50.0%
835256
50.0%

Unnamed: 138
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
LUTTE OUVRIÈRE
35256 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters493584
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLUTTE OUVRIÈRE
2nd rowLUTTE OUVRIÈRE
3rd rowLUTTE OUVRIÈRE
4th rowLUTTE OUVRIÈRE
5th rowLUTTE OUVRIÈRE
ValueCountFrequency (%)
LUTTE OUVRIÈRE35256
100.0%
2021-02-18T22:26:46.464839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:46.514314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
lutte35256
50.0%
ouvrière35256
50.0%

Most occurring characters

ValueCountFrequency (%)
U70512
14.3%
T70512
14.3%
E70512
14.3%
R70512
14.3%
L35256
7.1%
35256
7.1%
O35256
7.1%
V35256
7.1%
I35256
7.1%
È35256
7.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter458328
92.9%
Space Separator35256
 
7.1%

Most frequent character per category

ValueCountFrequency (%)
U70512
15.4%
T70512
15.4%
E70512
15.4%
R70512
15.4%
L35256
7.7%
O35256
7.7%
V35256
7.7%
I35256
7.7%
È35256
7.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin458328
92.9%
Common35256
 
7.1%

Most frequent character per script

ValueCountFrequency (%)
U70512
15.4%
T70512
15.4%
E70512
15.4%
R70512
15.4%
L35256
7.7%
O35256
7.7%
V35256
7.7%
I35256
7.7%
È35256
7.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII458328
92.9%
None35256
 
7.1%

Most frequent character per block

ValueCountFrequency (%)
U70512
15.4%
T70512
15.4%
E70512
15.4%
R70512
15.4%
L35256
7.7%
35256
7.7%
O35256
7.7%
V35256
7.7%
I35256
7.7%
ValueCountFrequency (%)
È35256
100.0%

Unnamed: 139
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
LUTTE OUVRIÈRE - CONTRE LE GRAND CAPITAL, LE CAMP DES TRAVAILLEURS
35256 

Length

Max length66
Median length66
Mean length66
Min length66

Characters and Unicode

Total characters2326896
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLUTTE OUVRIÈRE - CONTRE LE GRAND CAPITAL, LE CAMP DES TRAVAILLEURS
2nd rowLUTTE OUVRIÈRE - CONTRE LE GRAND CAPITAL, LE CAMP DES TRAVAILLEURS
3rd rowLUTTE OUVRIÈRE - CONTRE LE GRAND CAPITAL, LE CAMP DES TRAVAILLEURS
4th rowLUTTE OUVRIÈRE - CONTRE LE GRAND CAPITAL, LE CAMP DES TRAVAILLEURS
5th rowLUTTE OUVRIÈRE - CONTRE LE GRAND CAPITAL, LE CAMP DES TRAVAILLEURS
ValueCountFrequency (%)
LUTTE OUVRIÈRE - CONTRE LE GRAND CAPITAL, LE CAMP DES TRAVAILLEURS35256
100.0%
2021-02-18T22:26:46.635560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:46.685524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
le70512
18.2%
des35256
9.1%
ouvrière35256
9.1%
contre35256
9.1%
capital35256
9.1%
camp35256
9.1%
lutte35256
9.1%
grand35256
9.1%
travailleurs35256
9.1%
35256
9.1%

Most occurring characters

ValueCountFrequency (%)
352560
15.2%
E246792
10.6%
L211536
 
9.1%
R211536
 
9.1%
A211536
 
9.1%
T176280
 
7.6%
U105768
 
4.5%
I105768
 
4.5%
C105768
 
4.5%
O70512
 
3.0%
Other values (10)528840
22.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1903824
81.8%
Space Separator352560
 
15.2%
Dash Punctuation35256
 
1.5%
Other Punctuation35256
 
1.5%

Most frequent character per category

ValueCountFrequency (%)
E246792
13.0%
L211536
11.1%
R211536
11.1%
A211536
11.1%
T176280
9.3%
U105768
 
5.6%
I105768
 
5.6%
C105768
 
5.6%
O70512
 
3.7%
V70512
 
3.7%
Other values (7)387816
20.4%
ValueCountFrequency (%)
352560
100.0%
ValueCountFrequency (%)
-35256
100.0%
ValueCountFrequency (%)
,35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1903824
81.8%
Common423072
 
18.2%

Most frequent character per script

ValueCountFrequency (%)
E246792
13.0%
L211536
11.1%
R211536
11.1%
A211536
11.1%
T176280
9.3%
U105768
 
5.6%
I105768
 
5.6%
C105768
 
5.6%
O70512
 
3.7%
V70512
 
3.7%
Other values (7)387816
20.4%
ValueCountFrequency (%)
352560
83.3%
-35256
 
8.3%
,35256
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2291640
98.5%
None35256
 
1.5%

Most frequent character per block

ValueCountFrequency (%)
352560
15.4%
E246792
10.8%
L211536
9.2%
R211536
9.2%
A211536
9.2%
T176280
 
7.7%
U105768
 
4.6%
I105768
 
4.6%
C105768
 
4.6%
O70512
 
3.1%
Other values (9)493584
21.5%
ValueCountFrequency (%)
È35256
100.0%

Unnamed: 140
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
ARTHAUD Nathalie
35256 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters564096
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowARTHAUD Nathalie
2nd rowARTHAUD Nathalie
3rd rowARTHAUD Nathalie
4th rowARTHAUD Nathalie
5th rowARTHAUD Nathalie
ValueCountFrequency (%)
ARTHAUD Nathalie35256
100.0%
2021-02-18T22:26:46.811729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:46.871032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
nathalie35256
50.0%
arthaud35256
50.0%

Most occurring characters

ValueCountFrequency (%)
A70512
12.5%
a70512
12.5%
R35256
 
6.2%
T35256
 
6.2%
H35256
 
6.2%
U35256
 
6.2%
D35256
 
6.2%
35256
 
6.2%
N35256
 
6.2%
t35256
 
6.2%
Other values (4)141024
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter282048
50.0%
Lowercase Letter246792
43.8%
Space Separator35256
 
6.2%

Most frequent character per category

ValueCountFrequency (%)
A70512
25.0%
R35256
12.5%
T35256
12.5%
H35256
12.5%
U35256
12.5%
D35256
12.5%
N35256
12.5%
ValueCountFrequency (%)
a70512
28.6%
t35256
14.3%
h35256
14.3%
l35256
14.3%
i35256
14.3%
e35256
14.3%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin528840
93.8%
Common35256
 
6.2%

Most frequent character per script

ValueCountFrequency (%)
A70512
13.3%
a70512
13.3%
R35256
 
6.7%
T35256
 
6.7%
H35256
 
6.7%
U35256
 
6.7%
D35256
 
6.7%
N35256
 
6.7%
t35256
 
6.7%
h35256
 
6.7%
Other values (3)105768
20.0%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII564096
100.0%

Most frequent character per block

ValueCountFrequency (%)
A70512
12.5%
a70512
12.5%
R35256
 
6.2%
T35256
 
6.2%
H35256
 
6.2%
U35256
 
6.2%
D35256
 
6.2%
35256
 
6.2%
N35256
 
6.2%
t35256
 
6.2%
Other values (4)141024
25.0%

Unnamed: 141
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct193
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.001673474
Minimum0
Maximum2903
Zeros10532
Zeros (%)29.9%
Memory size275.6 KiB
2021-02-18T22:26:46.933522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile18
Maximum2903
Range2903
Interquartile range (IQR)4

Descriptive statistics

Standard deviation22.86199581
Coefficient of variation (CV)4.570869316
Kurtosis7542.196878
Mean5.001673474
Median Absolute Deviation (MAD)2
Skewness65.93600168
Sum176339
Variance522.6708524
MonotocityNot monotonic
2021-02-18T22:26:47.043698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010532
29.9%
16937
19.7%
24405
12.5%
32946
 
8.4%
42070
 
5.9%
51463
 
4.1%
61091
 
3.1%
7873
 
2.5%
8627
 
1.8%
9502
 
1.4%
Other values (183)3810
 
10.8%
ValueCountFrequency (%)
010532
29.9%
16937
19.7%
24405
12.5%
32946
 
8.4%
42070
 
5.9%
ValueCountFrequency (%)
29031
< 0.1%
10051
< 0.1%
7261
< 0.1%
6991
< 0.1%
6081
< 0.1%

Unnamed: 142
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct391
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4710982528
Minimum0
Maximum17.95
Zeros10532
Zeros (%)29.9%
Memory size275.6 KiB
2021-02-18T22:26:47.182709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.35
Q30.66
95-th percentile1.47
Maximum17.95
Range17.95
Interquartile range (IQR)0.66

Descriptive statistics

Standard deviation0.5856017582
Coefficient of variation (CV)1.243056528
Kurtosis45.98629265
Mean0.4710982528
Median Absolute Deviation (MAD)0.35
Skewness3.984256476
Sum16609.04
Variance0.3429294192
MonotocityNot monotonic
2021-02-18T22:26:47.328132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010532
29.9%
0.4384
 
1.1%
0.38354
 
1.0%
0.36353
 
1.0%
0.35350
 
1.0%
0.43349
 
1.0%
0.37348
 
1.0%
0.34347
 
1.0%
0.31341
 
1.0%
0.28340
 
1.0%
Other values (381)21558
61.1%
ValueCountFrequency (%)
010532
29.9%
0.016
 
< 0.1%
0.0218
 
0.1%
0.0315
 
< 0.1%
0.0425
 
0.1%
ValueCountFrequency (%)
17.951
< 0.1%
13.041
< 0.1%
9.091
< 0.1%
8.331
< 0.1%
7.251
< 0.1%

Unnamed: 143
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct561
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8940991604
Minimum0
Maximum31.82
Zeros10532
Zeros (%)29.9%
Memory size275.6 KiB
2021-02-18T22:26:47.468440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.69
Q31.27
95-th percentile2.74
Maximum31.82
Range31.82
Interquartile range (IQR)1.27

Descriptive statistics

Standard deviation1.073220761
Coefficient of variation (CV)1.200337511
Kurtosis38.76454627
Mean0.8940991604
Median Absolute Deviation (MAD)0.69
Skewness3.608519043
Sum31522.36
Variance1.151802802
MonotocityNot monotonic
2021-02-18T22:26:47.598043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010532
29.9%
0.68214
 
0.6%
0.72211
 
0.6%
0.56207
 
0.6%
0.75202
 
0.6%
0.74199
 
0.6%
0.78195
 
0.6%
0.71192
 
0.5%
0.83192
 
0.5%
0.5186
 
0.5%
Other values (551)22926
65.0%
ValueCountFrequency (%)
010532
29.9%
0.021
 
< 0.1%
0.032
 
< 0.1%
0.041
 
< 0.1%
0.053
 
< 0.1%
ValueCountFrequency (%)
31.821
< 0.1%
201
< 0.1%
18.751
< 0.1%
16.671
< 0.1%
14.811
< 0.1%

Unnamed: 144
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
19
35256 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters70512
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row19
2nd row19
3rd row19
4th row19
5th row19
ValueCountFrequency (%)
1935256
100.0%
2021-02-18T22:26:47.766038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:47.815419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1935256
100.0%

Most occurring characters

ValueCountFrequency (%)
135256
50.0%
935256
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number70512
100.0%

Most frequent character per category

ValueCountFrequency (%)
135256
50.0%
935256
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common70512
100.0%

Most frequent character per script

ValueCountFrequency (%)
135256
50.0%
935256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70512
100.0%

Most frequent character per block

ValueCountFrequency (%)
135256
50.0%
935256
50.0%

Unnamed: 145
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
POUR L'EUROPE DES GENS
35256 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters775632
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPOUR L'EUROPE DES GENS
2nd rowPOUR L'EUROPE DES GENS
3rd rowPOUR L'EUROPE DES GENS
4th rowPOUR L'EUROPE DES GENS
5th rowPOUR L'EUROPE DES GENS
ValueCountFrequency (%)
POUR L'EUROPE DES GENS35256
100.0%
2021-02-18T22:26:47.937141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:47.986481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
pour35256
25.0%
l'europe35256
25.0%
des35256
25.0%
gens35256
25.0%

Most occurring characters

ValueCountFrequency (%)
E141024
18.2%
105768
13.6%
P70512
9.1%
O70512
9.1%
U70512
9.1%
R70512
9.1%
S70512
9.1%
L35256
 
4.5%
'35256
 
4.5%
D35256
 
4.5%
Other values (2)70512
9.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter634608
81.8%
Space Separator105768
 
13.6%
Other Punctuation35256
 
4.5%

Most frequent character per category

ValueCountFrequency (%)
E141024
22.2%
P70512
11.1%
O70512
11.1%
U70512
11.1%
R70512
11.1%
S70512
11.1%
L35256
 
5.6%
D35256
 
5.6%
G35256
 
5.6%
N35256
 
5.6%
ValueCountFrequency (%)
105768
100.0%
ValueCountFrequency (%)
'35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin634608
81.8%
Common141024
 
18.2%

Most frequent character per script

ValueCountFrequency (%)
E141024
22.2%
P70512
11.1%
O70512
11.1%
U70512
11.1%
R70512
11.1%
S70512
11.1%
L35256
 
5.6%
D35256
 
5.6%
G35256
 
5.6%
N35256
 
5.6%
ValueCountFrequency (%)
105768
75.0%
'35256
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII775632
100.0%

Most frequent character per block

ValueCountFrequency (%)
E141024
18.2%
105768
13.6%
P70512
9.1%
O70512
9.1%
U70512
9.1%
R70512
9.1%
S70512
9.1%
L35256
 
4.5%
'35256
 
4.5%
D35256
 
4.5%
Other values (2)70512
9.1%

Unnamed: 146
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
POUR L'EUROPE DES GENS CONTRE L'EUROPE DE L'ARGENT
35256 

Length

Max length50
Median length50
Mean length50
Min length50

Characters and Unicode

Total characters1762800
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPOUR L'EUROPE DES GENS CONTRE L'EUROPE DE L'ARGENT
2nd rowPOUR L'EUROPE DES GENS CONTRE L'EUROPE DE L'ARGENT
3rd rowPOUR L'EUROPE DES GENS CONTRE L'EUROPE DE L'ARGENT
4th rowPOUR L'EUROPE DES GENS CONTRE L'EUROPE DE L'ARGENT
5th rowPOUR L'EUROPE DES GENS CONTRE L'EUROPE DE L'ARGENT
ValueCountFrequency (%)
POUR L'EUROPE DES GENS CONTRE L'EUROPE DE L'ARGENT35256
100.0%
2021-02-18T22:26:48.107522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:48.157199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
l'europe70512
25.0%
de35256
12.5%
pour35256
12.5%
des35256
12.5%
gens35256
12.5%
l'argent35256
12.5%
contre35256
12.5%

Most occurring characters

ValueCountFrequency (%)
E317304
18.0%
246792
14.0%
R176280
10.0%
O141024
8.0%
P105768
 
6.0%
U105768
 
6.0%
L105768
 
6.0%
'105768
 
6.0%
N105768
 
6.0%
D70512
 
4.0%
Other values (5)282048
16.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1410240
80.0%
Space Separator246792
 
14.0%
Other Punctuation105768
 
6.0%

Most frequent character per category

ValueCountFrequency (%)
E317304
22.5%
R176280
12.5%
O141024
10.0%
P105768
 
7.5%
U105768
 
7.5%
L105768
 
7.5%
N105768
 
7.5%
D70512
 
5.0%
S70512
 
5.0%
G70512
 
5.0%
Other values (3)141024
10.0%
ValueCountFrequency (%)
246792
100.0%
ValueCountFrequency (%)
'105768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1410240
80.0%
Common352560
 
20.0%

Most frequent character per script

ValueCountFrequency (%)
E317304
22.5%
R176280
12.5%
O141024
10.0%
P105768
 
7.5%
U105768
 
7.5%
L105768
 
7.5%
N105768
 
7.5%
D70512
 
5.0%
S70512
 
5.0%
G70512
 
5.0%
Other values (3)141024
10.0%
ValueCountFrequency (%)
246792
70.0%
'105768
30.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1762800
100.0%

Most frequent character per block

ValueCountFrequency (%)
E317304
18.0%
246792
14.0%
R176280
10.0%
O141024
8.0%
P105768
 
6.0%
U105768
 
6.0%
L105768
 
6.0%
'105768
 
6.0%
N105768
 
6.0%
D70512
 
4.0%
Other values (5)282048
16.0%

Unnamed: 147
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
BROSSAT Ian
35256 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters387816
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBROSSAT Ian
2nd rowBROSSAT Ian
3rd rowBROSSAT Ian
4th rowBROSSAT Ian
5th rowBROSSAT Ian
ValueCountFrequency (%)
BROSSAT Ian35256
100.0%
2021-02-18T22:26:48.279622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:48.328806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
brossat35256
50.0%
ian35256
50.0%

Most occurring characters

ValueCountFrequency (%)
S70512
18.2%
B35256
9.1%
R35256
9.1%
O35256
9.1%
A35256
9.1%
T35256
9.1%
35256
9.1%
I35256
9.1%
a35256
9.1%
n35256
9.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter282048
72.7%
Lowercase Letter70512
 
18.2%
Space Separator35256
 
9.1%

Most frequent character per category

ValueCountFrequency (%)
S70512
25.0%
B35256
12.5%
R35256
12.5%
O35256
12.5%
A35256
12.5%
T35256
12.5%
I35256
12.5%
ValueCountFrequency (%)
a35256
50.0%
n35256
50.0%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin352560
90.9%
Common35256
 
9.1%

Most frequent character per script

ValueCountFrequency (%)
S70512
20.0%
B35256
10.0%
R35256
10.0%
O35256
10.0%
A35256
10.0%
T35256
10.0%
I35256
10.0%
a35256
10.0%
n35256
10.0%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII387816
100.0%

Most frequent character per block

ValueCountFrequency (%)
S70512
18.2%
B35256
9.1%
R35256
9.1%
O35256
9.1%
A35256
9.1%
T35256
9.1%
35256
9.1%
I35256
9.1%
a35256
9.1%
n35256
9.1%

Unnamed: 148
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct451
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.02419446
Minimum0
Maximum23655
Zeros6048
Zeros (%)17.2%
Memory size275.6 KiB
2021-02-18T22:26:48.385652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q310
95-th percentile52
Maximum23655
Range23655
Interquartile range (IQR)9

Descriptive statistics

Standard deviation148.7075779
Coefficient of variation (CV)9.280190544
Kurtosis18302.54559
Mean16.02419446
Median Absolute Deviation (MAD)3
Skewness119.2670946
Sum564949
Variance22113.94373
MonotocityNot monotonic
2021-02-18T22:26:48.485441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06048
17.2%
14518
12.8%
23840
10.9%
32846
 
8.1%
42334
 
6.6%
51930
 
5.5%
61477
 
4.2%
71241
 
3.5%
81035
 
2.9%
9905
 
2.6%
Other values (441)9082
25.8%
ValueCountFrequency (%)
06048
17.2%
14518
12.8%
23840
10.9%
32846
8.1%
42334
 
6.6%
ValueCountFrequency (%)
236551
< 0.1%
72851
< 0.1%
34291
< 0.1%
31781
< 0.1%
25981
< 0.1%

Unnamed: 149
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct843
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.200477082
Minimum0
Maximum43.55
Zeros6048
Zeros (%)17.2%
Memory size275.6 KiB
2021-02-18T22:26:48.588355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.41
median0.9
Q31.55
95-th percentile3.46
Maximum43.55
Range43.55
Interquartile range (IQR)1.14

Descriptive statistics

Standard deviation1.412966527
Coefficient of variation (CV)1.177004166
Kurtosis65.1213438
Mean1.200477082
Median Absolute Deviation (MAD)0.56
Skewness5.077336359
Sum42324.02
Variance1.996474406
MonotocityNot monotonic
2021-02-18T22:26:48.690399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06048
 
17.2%
0.83222
 
0.6%
0.74221
 
0.6%
0.85214
 
0.6%
0.78214
 
0.6%
0.7213
 
0.6%
0.68213
 
0.6%
0.72211
 
0.6%
0.66206
 
0.6%
0.63204
 
0.6%
Other values (833)27290
77.4%
ValueCountFrequency (%)
06048
17.2%
0.011
 
< 0.1%
0.022
 
< 0.1%
0.031
 
< 0.1%
0.045
 
< 0.1%
ValueCountFrequency (%)
43.551
< 0.1%
35.941
< 0.1%
25.581
< 0.1%
25.291
< 0.1%
24.241
< 0.1%

Unnamed: 150
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1254
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.24457681
Minimum0
Maximum71.05
Zeros6048
Zeros (%)17.2%
Memory size275.6 KiB
2021-02-18T22:26:48.791161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.81
median1.72
Q32.9
95-th percentile6.3625
Maximum71.05
Range71.05
Interquartile range (IQR)2.09

Descriptive statistics

Standard deviation2.532597822
Coefficient of variation (CV)1.128318626
Kurtosis51.58473749
Mean2.24457681
Median Absolute Deviation (MAD)1.03
Skewness4.598084729
Sum79134.8
Variance6.414051728
MonotocityNot monotonic
2021-02-18T22:26:48.891270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06048
 
17.2%
1.52153
 
0.4%
1.75152
 
0.4%
1.85150
 
0.4%
1.56147
 
0.4%
1.82147
 
0.4%
1.45144
 
0.4%
1.92141
 
0.4%
2.27139
 
0.4%
2.22138
 
0.4%
Other values (1244)27897
79.1%
ValueCountFrequency (%)
06048
17.2%
0.092
 
< 0.1%
0.111
 
< 0.1%
0.121
 
< 0.1%
0.141
 
< 0.1%
ValueCountFrequency (%)
71.051
< 0.1%
58.971
< 0.1%
45.081
< 0.1%
44.441
< 0.1%
401
< 0.1%

Unnamed: 151
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
20
35256 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters70512
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20
2nd row20
3rd row20
4th row20
5th row20
ValueCountFrequency (%)
2035256
100.0%
2021-02-18T22:26:49.058646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:49.107095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2035256
100.0%

Most occurring characters

ValueCountFrequency (%)
235256
50.0%
035256
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number70512
100.0%

Most frequent character per category

ValueCountFrequency (%)
235256
50.0%
035256
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common70512
100.0%

Most frequent character per script

ValueCountFrequency (%)
235256
50.0%
035256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70512
100.0%

Most frequent character per block

ValueCountFrequency (%)
235256
50.0%
035256
50.0%

Unnamed: 152
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
ENSEMBLE POUR LE FREXIT
35256 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters810888
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowENSEMBLE POUR LE FREXIT
2nd rowENSEMBLE POUR LE FREXIT
3rd rowENSEMBLE POUR LE FREXIT
4th rowENSEMBLE POUR LE FREXIT
5th rowENSEMBLE POUR LE FREXIT
ValueCountFrequency (%)
ENSEMBLE POUR LE FREXIT35256
100.0%
2021-02-18T22:26:49.226886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:49.276145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
ensemble35256
25.0%
pour35256
25.0%
frexit35256
25.0%
le35256
25.0%

Most occurring characters

ValueCountFrequency (%)
E176280
21.7%
105768
13.0%
L70512
 
8.7%
R70512
 
8.7%
N35256
 
4.3%
S35256
 
4.3%
M35256
 
4.3%
B35256
 
4.3%
P35256
 
4.3%
O35256
 
4.3%
Other values (5)176280
21.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter705120
87.0%
Space Separator105768
 
13.0%

Most frequent character per category

ValueCountFrequency (%)
E176280
25.0%
L70512
 
10.0%
R70512
 
10.0%
N35256
 
5.0%
S35256
 
5.0%
M35256
 
5.0%
B35256
 
5.0%
P35256
 
5.0%
O35256
 
5.0%
U35256
 
5.0%
Other values (4)141024
20.0%
ValueCountFrequency (%)
105768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin705120
87.0%
Common105768
 
13.0%

Most frequent character per script

ValueCountFrequency (%)
E176280
25.0%
L70512
 
10.0%
R70512
 
10.0%
N35256
 
5.0%
S35256
 
5.0%
M35256
 
5.0%
B35256
 
5.0%
P35256
 
5.0%
O35256
 
5.0%
U35256
 
5.0%
Other values (4)141024
20.0%
ValueCountFrequency (%)
105768
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII810888
100.0%

Most frequent character per block

ValueCountFrequency (%)
E176280
21.7%
105768
13.0%
L70512
 
8.7%
R70512
 
8.7%
N35256
 
4.3%
S35256
 
4.3%
M35256
 
4.3%
B35256
 
4.3%
P35256
 
4.3%
O35256
 
4.3%
Other values (5)176280
21.7%

Unnamed: 153
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
ENSEMBLE POUR LE FREXIT
35256 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters810888
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowENSEMBLE POUR LE FREXIT
2nd rowENSEMBLE POUR LE FREXIT
3rd rowENSEMBLE POUR LE FREXIT
4th rowENSEMBLE POUR LE FREXIT
5th rowENSEMBLE POUR LE FREXIT
ValueCountFrequency (%)
ENSEMBLE POUR LE FREXIT35256
100.0%
2021-02-18T22:26:49.399722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:49.451190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
ensemble35256
25.0%
pour35256
25.0%
frexit35256
25.0%
le35256
25.0%

Most occurring characters

ValueCountFrequency (%)
E176280
21.7%
105768
13.0%
L70512
 
8.7%
R70512
 
8.7%
N35256
 
4.3%
S35256
 
4.3%
M35256
 
4.3%
B35256
 
4.3%
P35256
 
4.3%
O35256
 
4.3%
Other values (5)176280
21.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter705120
87.0%
Space Separator105768
 
13.0%

Most frequent character per category

ValueCountFrequency (%)
E176280
25.0%
L70512
 
10.0%
R70512
 
10.0%
N35256
 
5.0%
S35256
 
5.0%
M35256
 
5.0%
B35256
 
5.0%
P35256
 
5.0%
O35256
 
5.0%
U35256
 
5.0%
Other values (4)141024
20.0%
ValueCountFrequency (%)
105768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin705120
87.0%
Common105768
 
13.0%

Most frequent character per script

ValueCountFrequency (%)
E176280
25.0%
L70512
 
10.0%
R70512
 
10.0%
N35256
 
5.0%
S35256
 
5.0%
M35256
 
5.0%
B35256
 
5.0%
P35256
 
5.0%
O35256
 
5.0%
U35256
 
5.0%
Other values (4)141024
20.0%
ValueCountFrequency (%)
105768
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII810888
100.0%

Most frequent character per block

ValueCountFrequency (%)
E176280
21.7%
105768
13.0%
L70512
 
8.7%
R70512
 
8.7%
N35256
 
4.3%
S35256
 
4.3%
M35256
 
4.3%
B35256
 
4.3%
P35256
 
4.3%
O35256
 
4.3%
Other values (5)176280
21.7%

Unnamed: 154
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
ASSELINEAU François
35256 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters669864
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowASSELINEAU François
2nd rowASSELINEAU François
3rd rowASSELINEAU François
4th rowASSELINEAU François
5th rowASSELINEAU François
ValueCountFrequency (%)
ASSELINEAU François35256
100.0%
2021-02-18T22:26:49.578017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:49.629772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
asselineau35256
50.0%
françois35256
50.0%

Most occurring characters

ValueCountFrequency (%)
A70512
 
10.5%
S70512
 
10.5%
E70512
 
10.5%
L35256
 
5.3%
I35256
 
5.3%
N35256
 
5.3%
U35256
 
5.3%
35256
 
5.3%
F35256
 
5.3%
r35256
 
5.3%
Other values (6)211536
31.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter387816
57.9%
Lowercase Letter246792
36.8%
Space Separator35256
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
A70512
18.2%
S70512
18.2%
E70512
18.2%
L35256
9.1%
I35256
9.1%
N35256
9.1%
U35256
9.1%
F35256
9.1%
ValueCountFrequency (%)
r35256
14.3%
a35256
14.3%
n35256
14.3%
ç35256
14.3%
o35256
14.3%
i35256
14.3%
s35256
14.3%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin634608
94.7%
Common35256
 
5.3%

Most frequent character per script

ValueCountFrequency (%)
A70512
 
11.1%
S70512
 
11.1%
E70512
 
11.1%
L35256
 
5.6%
I35256
 
5.6%
N35256
 
5.6%
U35256
 
5.6%
F35256
 
5.6%
r35256
 
5.6%
a35256
 
5.6%
Other values (5)176280
27.8%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII634608
94.7%
None35256
 
5.3%

Most frequent character per block

ValueCountFrequency (%)
A70512
 
11.1%
S70512
 
11.1%
E70512
 
11.1%
L35256
 
5.6%
I35256
 
5.6%
N35256
 
5.6%
U35256
 
5.6%
35256
 
5.6%
F35256
 
5.6%
r35256
 
5.6%
Other values (5)176280
27.8%
ValueCountFrequency (%)
ç35256
100.0%

Unnamed: 155
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct282
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.529753801
Minimum0
Maximum7647
Zeros9403
Zeros (%)26.7%
Memory size275.6 KiB
2021-02-18T22:26:49.693624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile26
Maximum7647
Range7647
Interquartile range (IQR)5

Descriptive statistics

Standard deviation52.11245077
Coefficient of variation (CV)6.920870475
Kurtosis13407.73333
Mean7.529753801
Median Absolute Deviation (MAD)2
Skewness97.33916481
Sum265469
Variance2715.707525
MonotocityNot monotonic
2021-02-18T22:26:49.805503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09403
26.7%
15990
17.0%
24313
12.2%
33065
 
8.7%
42261
 
6.4%
51606
 
4.6%
61244
 
3.5%
7930
 
2.6%
8809
 
2.3%
9581
 
1.6%
Other values (272)5054
14.3%
ValueCountFrequency (%)
09403
26.7%
15990
17.0%
24313
12.2%
33065
 
8.7%
42261
 
6.4%
ValueCountFrequency (%)
76471
< 0.1%
28191
< 0.1%
15171
< 0.1%
14381
< 0.1%
13361
< 0.1%

Unnamed: 156
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct473
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6066504992
Minimum0
Maximum18.18
Zeros9403
Zeros (%)26.7%
Memory size275.6 KiB
2021-02-18T22:26:49.921477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.47
Q30.81
95-th percentile1.85
Maximum18.18
Range18.18
Interquartile range (IQR)0.81

Descriptive statistics

Standard deviation0.7436879617
Coefficient of variation (CV)1.225891947
Kurtosis34.24413293
Mean0.6066504992
Median Absolute Deviation (MAD)0.38
Skewness3.926866091
Sum21388.07
Variance0.5530717844
MonotocityNot monotonic
2021-02-18T22:26:50.030294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09403
26.7%
0.52341
 
1.0%
0.43336
 
1.0%
0.4331
 
0.9%
0.51324
 
0.9%
0.38320
 
0.9%
0.5320
 
0.9%
0.49318
 
0.9%
0.56317
 
0.9%
0.48316
 
0.9%
Other values (463)22930
65.0%
ValueCountFrequency (%)
09403
26.7%
0.033
 
< 0.1%
0.045
 
< 0.1%
0.055
 
< 0.1%
0.068
 
< 0.1%
ValueCountFrequency (%)
18.181
< 0.1%
13.751
< 0.1%
11.761
< 0.1%
11.481
< 0.1%
11.112
< 0.1%

Unnamed: 157
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct654
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.14475295
Minimum0
Maximum25
Zeros9403
Zeros (%)26.7%
Memory size275.6 KiB
2021-02-18T22:26:50.142929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.91
Q31.57
95-th percentile3.41
Maximum25
Range25
Interquartile range (IQR)1.57

Descriptive statistics

Standard deviation1.320768939
Coefficient of variation (CV)1.15375893
Kurtosis23.74901446
Mean1.14475295
Median Absolute Deviation (MAD)0.73
Skewness3.303158944
Sum40359.41
Variance1.74443059
MonotocityNot monotonic
2021-02-18T22:26:50.254431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09403
 
26.7%
0.93206
 
0.6%
0.85206
 
0.6%
1.12190
 
0.5%
1.09187
 
0.5%
1.2187
 
0.5%
1.08184
 
0.5%
0.83183
 
0.5%
1182
 
0.5%
0.78181
 
0.5%
Other values (644)24147
68.5%
ValueCountFrequency (%)
09403
26.7%
0.081
 
< 0.1%
0.091
 
< 0.1%
0.12
 
< 0.1%
0.115
 
< 0.1%
ValueCountFrequency (%)
251
< 0.1%
221
< 0.1%
21.881
< 0.1%
202
< 0.1%
18.181
< 0.1%

Unnamed: 158
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
21
35256 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters70512
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row21
2nd row21
3rd row21
4th row21
5th row21
ValueCountFrequency (%)
2135256
100.0%
2021-02-18T22:26:50.430376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:50.482550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2135256
100.0%

Most occurring characters

ValueCountFrequency (%)
235256
50.0%
135256
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number70512
100.0%

Most frequent character per category

ValueCountFrequency (%)
235256
50.0%
135256
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common70512
100.0%

Most frequent character per script

ValueCountFrequency (%)
235256
50.0%
135256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70512
100.0%

Most frequent character per block

ValueCountFrequency (%)
235256
50.0%
135256
50.0%

Unnamed: 159
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
LISTE CITOYENNE
35256 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters528840
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLISTE CITOYENNE
2nd rowLISTE CITOYENNE
3rd rowLISTE CITOYENNE
4th rowLISTE CITOYENNE
5th rowLISTE CITOYENNE
ValueCountFrequency (%)
LISTE CITOYENNE35256
100.0%
2021-02-18T22:26:50.611344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:50.664257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
citoyenne35256
50.0%
liste35256
50.0%

Most occurring characters

ValueCountFrequency (%)
E105768
20.0%
I70512
13.3%
T70512
13.3%
N70512
13.3%
L35256
 
6.7%
S35256
 
6.7%
35256
 
6.7%
C35256
 
6.7%
O35256
 
6.7%
Y35256
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter493584
93.3%
Space Separator35256
 
6.7%

Most frequent character per category

ValueCountFrequency (%)
E105768
21.4%
I70512
14.3%
T70512
14.3%
N70512
14.3%
L35256
 
7.1%
S35256
 
7.1%
C35256
 
7.1%
O35256
 
7.1%
Y35256
 
7.1%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin493584
93.3%
Common35256
 
6.7%

Most frequent character per script

ValueCountFrequency (%)
E105768
21.4%
I70512
14.3%
T70512
14.3%
N70512
14.3%
L35256
 
7.1%
S35256
 
7.1%
C35256
 
7.1%
O35256
 
7.1%
Y35256
 
7.1%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII528840
100.0%

Most frequent character per block

ValueCountFrequency (%)
E105768
20.0%
I70512
13.3%
T70512
13.3%
N70512
13.3%
L35256
 
6.7%
S35256
 
6.7%
35256
 
6.7%
C35256
 
6.7%
O35256
 
6.7%
Y35256
 
6.7%

Unnamed: 160
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
LISTE CITOYENNE DU PRINTEMPS EUROPÉEN AVEC BENOÎT HAMON SOUTENUE PAR GÉNÉRATION.S ET DÈME-DIEM 25
35256 

Length

Max length97
Median length97
Mean length97
Min length97

Characters and Unicode

Total characters3419832
Distinct characters27
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLISTE CITOYENNE DU PRINTEMPS EUROPÉEN AVEC BENOÎT HAMON SOUTENUE PAR GÉNÉRATION.S ET DÈME-DIEM 25
2nd rowLISTE CITOYENNE DU PRINTEMPS EUROPÉEN AVEC BENOÎT HAMON SOUTENUE PAR GÉNÉRATION.S ET DÈME-DIEM 25
3rd rowLISTE CITOYENNE DU PRINTEMPS EUROPÉEN AVEC BENOÎT HAMON SOUTENUE PAR GÉNÉRATION.S ET DÈME-DIEM 25
4th rowLISTE CITOYENNE DU PRINTEMPS EUROPÉEN AVEC BENOÎT HAMON SOUTENUE PAR GÉNÉRATION.S ET DÈME-DIEM 25
5th rowLISTE CITOYENNE DU PRINTEMPS EUROPÉEN AVEC BENOÎT HAMON SOUTENUE PAR GÉNÉRATION.S ET DÈME-DIEM 25
ValueCountFrequency (%)
LISTE CITOYENNE DU PRINTEMPS EUROPÉEN AVEC BENOÎT HAMON SOUTENUE PAR GÉNÉRATION.S ET DÈME-DIEM 2535256
100.0%
2021-02-18T22:26:50.794176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:50.847824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
avec35256
 
7.1%
du35256
 
7.1%
liste35256
 
7.1%
et35256
 
7.1%
printemps35256
 
7.1%
hamon35256
 
7.1%
citoyenne35256
 
7.1%
2535256
 
7.1%
dème-diem35256
 
7.1%
par35256
 
7.1%
Other values (4)141024
28.6%

Most occurring characters

ValueCountFrequency (%)
E458328
13.4%
458328
13.4%
N317304
 
9.3%
T246792
 
7.2%
O211536
 
6.2%
I176280
 
5.2%
S141024
 
4.1%
U141024
 
4.1%
P141024
 
4.1%
R141024
 
4.1%
Other values (17)987168
28.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2820480
82.5%
Space Separator458328
 
13.4%
Decimal Number70512
 
2.1%
Other Punctuation35256
 
1.0%
Dash Punctuation35256
 
1.0%

Most frequent character per category

ValueCountFrequency (%)
E458328
16.2%
N317304
11.2%
T246792
 
8.8%
O211536
 
7.5%
I176280
 
6.2%
S141024
 
5.0%
U141024
 
5.0%
P141024
 
5.0%
R141024
 
5.0%
M141024
 
5.0%
Other values (12)705120
25.0%
ValueCountFrequency (%)
235256
50.0%
535256
50.0%
ValueCountFrequency (%)
458328
100.0%
ValueCountFrequency (%)
.35256
100.0%
ValueCountFrequency (%)
-35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2820480
82.5%
Common599352
 
17.5%

Most frequent character per script

ValueCountFrequency (%)
E458328
16.2%
N317304
11.2%
T246792
 
8.8%
O211536
 
7.5%
I176280
 
6.2%
S141024
 
5.0%
U141024
 
5.0%
P141024
 
5.0%
R141024
 
5.0%
M141024
 
5.0%
Other values (12)705120
25.0%
ValueCountFrequency (%)
458328
76.5%
.35256
 
5.9%
-35256
 
5.9%
235256
 
5.9%
535256
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII3243552
94.8%
None176280
 
5.2%

Most frequent character per block

ValueCountFrequency (%)
E458328
14.1%
458328
14.1%
N317304
 
9.8%
T246792
 
7.6%
O211536
 
6.5%
I176280
 
5.4%
S141024
 
4.3%
U141024
 
4.3%
P141024
 
4.3%
R141024
 
4.3%
Other values (14)810888
25.0%
ValueCountFrequency (%)
É105768
60.0%
Î35256
 
20.0%
È35256
 
20.0%

Unnamed: 161
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
HAMON Benoît
35256 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters423072
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHAMON Benoît
2nd rowHAMON Benoît
3rd rowHAMON Benoît
4th rowHAMON Benoît
5th rowHAMON Benoît
ValueCountFrequency (%)
HAMON Benoît35256
100.0%
2021-02-18T22:26:50.982301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:51.035451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
hamon35256
50.0%
benoît35256
50.0%

Most occurring characters

ValueCountFrequency (%)
H35256
8.3%
A35256
8.3%
M35256
8.3%
O35256
8.3%
N35256
8.3%
35256
8.3%
B35256
8.3%
e35256
8.3%
n35256
8.3%
o35256
8.3%
Other values (2)70512
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter211536
50.0%
Lowercase Letter176280
41.7%
Space Separator35256
 
8.3%

Most frequent character per category

ValueCountFrequency (%)
H35256
16.7%
A35256
16.7%
M35256
16.7%
O35256
16.7%
N35256
16.7%
B35256
16.7%
ValueCountFrequency (%)
e35256
20.0%
n35256
20.0%
o35256
20.0%
î35256
20.0%
t35256
20.0%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin387816
91.7%
Common35256
 
8.3%

Most frequent character per script

ValueCountFrequency (%)
H35256
9.1%
A35256
9.1%
M35256
9.1%
O35256
9.1%
N35256
9.1%
B35256
9.1%
e35256
9.1%
n35256
9.1%
o35256
9.1%
î35256
9.1%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII387816
91.7%
None35256
 
8.3%

Most frequent character per block

ValueCountFrequency (%)
H35256
9.1%
A35256
9.1%
M35256
9.1%
O35256
9.1%
N35256
9.1%
35256
9.1%
B35256
9.1%
e35256
9.1%
n35256
9.1%
o35256
9.1%
ValueCountFrequency (%)
î35256
100.0%

Unnamed: 162
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct515
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.0395961
Minimum0
Maximum32275
Zeros3838
Zeros (%)10.9%
Memory size275.6 KiB
2021-02-18T22:26:51.098511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q314
95-th percentile70
Maximum32275
Range32275
Interquartile range (IQR)12

Descriptive statistics

Standard deviation198.4052733
Coefficient of variation (CV)9.430089455
Kurtosis19908.40231
Mean21.0395961
Median Absolute Deviation (MAD)4
Skewness125.8328506
Sum741772
Variance39364.65247
MonotocityNot monotonic
2021-02-18T22:26:51.204443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03838
 
10.9%
13665
 
10.4%
23398
 
9.6%
32906
 
8.2%
42417
 
6.9%
52032
 
5.8%
61636
 
4.6%
71370
 
3.9%
81202
 
3.4%
9975
 
2.8%
Other values (505)11817
33.5%
ValueCountFrequency (%)
03838
10.9%
13665
10.4%
23398
9.6%
32906
8.2%
42417
6.9%
ValueCountFrequency (%)
322751
< 0.1%
64521
< 0.1%
59931
< 0.1%
58521
< 0.1%
46561
< 0.1%

Unnamed: 163
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct697
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.544156739
Minimum0
Maximum21.05
Zeros3838
Zeros (%)10.9%
Memory size275.6 KiB
2021-02-18T22:26:51.318454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.8
median1.37
Q32.04
95-th percentile3.64
Maximum21.05
Range21.05
Interquartile range (IQR)1.24

Descriptive statistics

Standard deviation1.223985969
Coefficient of variation (CV)0.7926565598
Kurtosis20.01673887
Mean1.544156739
Median Absolute Deviation (MAD)0.62
Skewness2.613340276
Sum54440.79
Variance1.498141652
MonotocityNot monotonic
2021-02-18T22:26:51.422341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03838
 
10.9%
1.2210
 
0.6%
1.08202
 
0.6%
1.27199
 
0.6%
1.18199
 
0.6%
1.52192
 
0.5%
1.23191
 
0.5%
1.12186
 
0.5%
1.32182
 
0.5%
0.93180
 
0.5%
Other values (687)29677
84.2%
ValueCountFrequency (%)
03838
10.9%
0.021
 
< 0.1%
0.052
 
< 0.1%
0.061
 
< 0.1%
0.071
 
< 0.1%
ValueCountFrequency (%)
21.051
< 0.1%
20.511
< 0.1%
19.511
< 0.1%
18.922
< 0.1%
18.751
< 0.1%

Unnamed: 164
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1035
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.895021557
Minimum0
Maximum40
Zeros3838
Zeros (%)10.9%
Memory size275.6 KiB
2021-02-18T22:26:51.529248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.55
median2.63
Q33.87
95-th percentile6.59
Maximum40
Range40
Interquartile range (IQR)2.32

Descriptive statistics

Standard deviation2.15170288
Coefficient of variation (CV)0.7432424381
Kurtosis14.12692864
Mean2.895021557
Median Absolute Deviation (MAD)1.15
Skewness2.08527862
Sum102066.88
Variance4.629825284
MonotocityNot monotonic
2021-02-18T22:26:51.638344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03838
 
10.9%
3.45172
 
0.5%
2.56171
 
0.5%
2.86167
 
0.5%
2.27161
 
0.5%
3.33161
 
0.5%
2.44159
 
0.5%
3.23159
 
0.5%
3.03158
 
0.4%
2.33155
 
0.4%
Other values (1025)29955
85.0%
ValueCountFrequency (%)
03838
10.9%
0.161
 
< 0.1%
0.171
 
< 0.1%
0.181
 
< 0.1%
0.21
 
< 0.1%
ValueCountFrequency (%)
401
< 0.1%
31.891
< 0.1%
31.581
< 0.1%
301
< 0.1%
29.631
< 0.1%

Unnamed: 165
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
22
35256 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters70512
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row22
2nd row22
3rd row22
4th row22
5th row22
ValueCountFrequency (%)
2235256
100.0%
2021-02-18T22:26:51.819394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:51.871590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2235256
100.0%

Most occurring characters

ValueCountFrequency (%)
270512
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number70512
100.0%

Most frequent character per category

ValueCountFrequency (%)
270512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common70512
100.0%

Most frequent character per script

ValueCountFrequency (%)
270512
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70512
100.0%

Most frequent character per block

ValueCountFrequency (%)
270512
100.0%

Unnamed: 166
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
À VOIX ÉGALES
35256 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters458328
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowÀ VOIX ÉGALES
2nd rowÀ VOIX ÉGALES
3rd rowÀ VOIX ÉGALES
4th rowÀ VOIX ÉGALES
5th rowÀ VOIX ÉGALES
ValueCountFrequency (%)
À VOIX ÉGALES35256
100.0%
2021-02-18T22:26:52.000527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:52.053547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
égales35256
33.3%
voix35256
33.3%
à35256
33.3%

Most occurring characters

ValueCountFrequency (%)
70512
15.4%
À35256
7.7%
V35256
7.7%
O35256
7.7%
I35256
7.7%
X35256
7.7%
É35256
7.7%
G35256
7.7%
A35256
7.7%
L35256
7.7%
Other values (2)70512
15.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter387816
84.6%
Space Separator70512
 
15.4%

Most frequent character per category

ValueCountFrequency (%)
À35256
9.1%
V35256
9.1%
O35256
9.1%
I35256
9.1%
X35256
9.1%
É35256
9.1%
G35256
9.1%
A35256
9.1%
L35256
9.1%
E35256
9.1%
ValueCountFrequency (%)
70512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin387816
84.6%
Common70512
 
15.4%

Most frequent character per script

ValueCountFrequency (%)
À35256
9.1%
V35256
9.1%
O35256
9.1%
I35256
9.1%
X35256
9.1%
É35256
9.1%
G35256
9.1%
A35256
9.1%
L35256
9.1%
E35256
9.1%
ValueCountFrequency (%)
70512
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII387816
84.6%
None70512
 
15.4%

Most frequent character per block

ValueCountFrequency (%)
À35256
50.0%
É35256
50.0%
ValueCountFrequency (%)
70512
18.2%
V35256
9.1%
O35256
9.1%
I35256
9.1%
X35256
9.1%
G35256
9.1%
A35256
9.1%
L35256
9.1%
E35256
9.1%
S35256
9.1%

Unnamed: 167
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
À VOIX ÉGALES
35256 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters458328
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowÀ VOIX ÉGALES
2nd rowÀ VOIX ÉGALES
3rd rowÀ VOIX ÉGALES
4th rowÀ VOIX ÉGALES
5th rowÀ VOIX ÉGALES
ValueCountFrequency (%)
À VOIX ÉGALES35256
100.0%
2021-02-18T22:26:52.181666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:52.233396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
égales35256
33.3%
voix35256
33.3%
à35256
33.3%

Most occurring characters

ValueCountFrequency (%)
70512
15.4%
À35256
7.7%
V35256
7.7%
O35256
7.7%
I35256
7.7%
X35256
7.7%
É35256
7.7%
G35256
7.7%
A35256
7.7%
L35256
7.7%
Other values (2)70512
15.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter387816
84.6%
Space Separator70512
 
15.4%

Most frequent character per category

ValueCountFrequency (%)
À35256
9.1%
V35256
9.1%
O35256
9.1%
I35256
9.1%
X35256
9.1%
É35256
9.1%
G35256
9.1%
A35256
9.1%
L35256
9.1%
E35256
9.1%
ValueCountFrequency (%)
70512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin387816
84.6%
Common70512
 
15.4%

Most frequent character per script

ValueCountFrequency (%)
À35256
9.1%
V35256
9.1%
O35256
9.1%
I35256
9.1%
X35256
9.1%
É35256
9.1%
G35256
9.1%
A35256
9.1%
L35256
9.1%
E35256
9.1%
ValueCountFrequency (%)
70512
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII387816
84.6%
None70512
 
15.4%

Most frequent character per block

ValueCountFrequency (%)
À35256
50.0%
É35256
50.0%
ValueCountFrequency (%)
70512
18.2%
V35256
9.1%
O35256
9.1%
I35256
9.1%
X35256
9.1%
G35256
9.1%
A35256
9.1%
L35256
9.1%
E35256
9.1%
S35256
9.1%

Unnamed: 168
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
TOMASINI Nathalie
35256 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters599352
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTOMASINI Nathalie
2nd rowTOMASINI Nathalie
3rd rowTOMASINI Nathalie
4th rowTOMASINI Nathalie
5th rowTOMASINI Nathalie
ValueCountFrequency (%)
TOMASINI Nathalie35256
100.0%
2021-02-18T22:26:52.362830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:52.415938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
tomasini35256
50.0%
nathalie35256
50.0%

Most occurring characters

ValueCountFrequency (%)
I70512
11.8%
N70512
11.8%
a70512
11.8%
T35256
 
5.9%
O35256
 
5.9%
M35256
 
5.9%
A35256
 
5.9%
S35256
 
5.9%
35256
 
5.9%
t35256
 
5.9%
Other values (4)141024
23.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter317304
52.9%
Lowercase Letter246792
41.2%
Space Separator35256
 
5.9%

Most frequent character per category

ValueCountFrequency (%)
I70512
22.2%
N70512
22.2%
T35256
11.1%
O35256
11.1%
M35256
11.1%
A35256
11.1%
S35256
11.1%
ValueCountFrequency (%)
a70512
28.6%
t35256
14.3%
h35256
14.3%
l35256
14.3%
i35256
14.3%
e35256
14.3%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin564096
94.1%
Common35256
 
5.9%

Most frequent character per script

ValueCountFrequency (%)
I70512
12.5%
N70512
12.5%
a70512
12.5%
T35256
 
6.2%
O35256
 
6.2%
M35256
 
6.2%
A35256
 
6.2%
S35256
 
6.2%
t35256
 
6.2%
h35256
 
6.2%
Other values (3)105768
18.8%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII599352
100.0%

Most frequent character per block

ValueCountFrequency (%)
I70512
11.8%
N70512
11.8%
a70512
11.8%
T35256
 
5.9%
O35256
 
5.9%
M35256
 
5.9%
A35256
 
5.9%
S35256
 
5.9%
35256
 
5.9%
t35256
 
5.9%
Other values (4)141024
23.5%

Unnamed: 169
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct55
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2219480372
Minimum0
Maximum694
Zeros33044
Zeros (%)93.7%
Memory size275.6 KiB
2021-02-18T22:26:52.479048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum694
Range694
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.126307657
Coefficient of variation (CV)18.59132304
Kurtosis22691.04657
Mean0.2219480372
Median Absolute Deviation (MAD)0
Skewness136.6421583
Sum7825
Variance17.02641488
MonotocityNot monotonic
2021-02-18T22:26:52.586754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033044
93.7%
11365
 
3.9%
2366
 
1.0%
3138
 
0.4%
459
 
0.2%
542
 
0.1%
724
 
0.1%
824
 
0.1%
622
 
0.1%
1015
 
< 0.1%
Other values (45)157
 
0.4%
ValueCountFrequency (%)
033044
93.7%
11365
 
3.9%
2366
 
1.0%
3138
 
0.4%
459
 
0.2%
ValueCountFrequency (%)
6941
< 0.1%
741
< 0.1%
702
< 0.1%
691
< 0.1%
631
< 0.1%

Unnamed: 170
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct147
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01296970728
Minimum0
Maximum5.6
Zeros33105
Zeros (%)93.9%
Memory size275.6 KiB
2021-02-18T22:26:52.697820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.02
Maximum5.6
Range5.6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1087490103
Coefficient of variation (CV)8.384846926
Kurtosis657.932774
Mean0.01296970728
Median Absolute Deviation (MAD)0
Skewness20.60114912
Sum457.26
Variance0.01182634723
MonotocityNot monotonic
2021-02-18T22:26:52.798600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033105
93.9%
0.01221
 
0.6%
0.02199
 
0.6%
0.03148
 
0.4%
0.04136
 
0.4%
0.05117
 
0.3%
0.0789
 
0.3%
0.0686
 
0.2%
0.0873
 
0.2%
0.0972
 
0.2%
Other values (137)1010
 
2.9%
ValueCountFrequency (%)
033105
93.9%
0.01221
 
0.6%
0.02199
 
0.6%
0.03148
 
0.4%
0.04136
 
0.4%
ValueCountFrequency (%)
5.61
< 0.1%
5.261
< 0.1%
4.031
< 0.1%
3.711
< 0.1%
3.451
< 0.1%

Unnamed: 171
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct202
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02385267756
Minimum0
Maximum9.09
Zeros33053
Zeros (%)93.8%
Memory size275.6 KiB
2021-02-18T22:26:52.910249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.05
Maximum9.09
Range9.09
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.191367715
Coefficient of variation (CV)8.02290286
Kurtosis542.7822727
Mean0.02385267756
Median Absolute Deviation (MAD)0
Skewness18.91566561
Sum840.95
Variance0.03662160235
MonotocityNot monotonic
2021-02-18T22:26:53.015444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033053
93.8%
0.03125
 
0.4%
0.02106
 
0.3%
0.0497
 
0.3%
0.0192
 
0.3%
0.0586
 
0.2%
0.0682
 
0.2%
0.0980
 
0.2%
0.0880
 
0.2%
0.0755
 
0.2%
Other values (192)1400
 
4.0%
ValueCountFrequency (%)
033053
93.8%
0.0192
 
0.3%
0.02106
 
0.3%
0.03125
 
0.4%
0.0497
 
0.3%
ValueCountFrequency (%)
9.091
< 0.1%
7.951
< 0.1%
7.351
< 0.1%
6.341
< 0.1%
6.151
< 0.1%

Unnamed: 172
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
23
35256 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters70512
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row23
2nd row23
3rd row23
4th row23
5th row23
ValueCountFrequency (%)
2335256
100.0%
2021-02-18T22:26:53.189504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:53.241063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2335256
100.0%

Most occurring characters

ValueCountFrequency (%)
235256
50.0%
335256
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number70512
100.0%

Most frequent character per category

ValueCountFrequency (%)
235256
50.0%
335256
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common70512
100.0%

Most frequent character per script

ValueCountFrequency (%)
235256
50.0%
335256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70512
100.0%

Most frequent character per block

ValueCountFrequency (%)
235256
50.0%
335256
50.0%

Unnamed: 173
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
PRENEZ LE POUVOIR
35256 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters599352
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPRENEZ LE POUVOIR
2nd rowPRENEZ LE POUVOIR
3rd rowPRENEZ LE POUVOIR
4th rowPRENEZ LE POUVOIR
5th rowPRENEZ LE POUVOIR
ValueCountFrequency (%)
PRENEZ LE POUVOIR35256
100.0%
2021-02-18T22:26:53.366589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:53.419290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
pouvoir35256
33.3%
prenez35256
33.3%
le35256
33.3%

Most occurring characters

ValueCountFrequency (%)
E105768
17.6%
P70512
11.8%
R70512
11.8%
70512
11.8%
O70512
11.8%
N35256
 
5.9%
Z35256
 
5.9%
L35256
 
5.9%
U35256
 
5.9%
V35256
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter528840
88.2%
Space Separator70512
 
11.8%

Most frequent character per category

ValueCountFrequency (%)
E105768
20.0%
P70512
13.3%
R70512
13.3%
O70512
13.3%
N35256
 
6.7%
Z35256
 
6.7%
L35256
 
6.7%
U35256
 
6.7%
V35256
 
6.7%
I35256
 
6.7%
ValueCountFrequency (%)
70512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin528840
88.2%
Common70512
 
11.8%

Most frequent character per script

ValueCountFrequency (%)
E105768
20.0%
P70512
13.3%
R70512
13.3%
O70512
13.3%
N35256
 
6.7%
Z35256
 
6.7%
L35256
 
6.7%
U35256
 
6.7%
V35256
 
6.7%
I35256
 
6.7%
ValueCountFrequency (%)
70512
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII599352
100.0%

Most frequent character per block

ValueCountFrequency (%)
E105768
17.6%
P70512
11.8%
R70512
11.8%
70512
11.8%
O70512
11.8%
N35256
 
5.9%
Z35256
 
5.9%
L35256
 
5.9%
U35256
 
5.9%
V35256
 
5.9%

Unnamed: 174
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
PRENEZ LE POUVOIR, LISTE SOUTENUE PAR MARINE LE PEN
35256 

Length

Max length51
Median length51
Mean length51
Min length51

Characters and Unicode

Total characters1798056
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPRENEZ LE POUVOIR, LISTE SOUTENUE PAR MARINE LE PEN
2nd rowPRENEZ LE POUVOIR, LISTE SOUTENUE PAR MARINE LE PEN
3rd rowPRENEZ LE POUVOIR, LISTE SOUTENUE PAR MARINE LE PEN
4th rowPRENEZ LE POUVOIR, LISTE SOUTENUE PAR MARINE LE PEN
5th rowPRENEZ LE POUVOIR, LISTE SOUTENUE PAR MARINE LE PEN
ValueCountFrequency (%)
PRENEZ LE POUVOIR, LISTE SOUTENUE PAR MARINE LE PEN35256
100.0%
2021-02-18T22:26:53.549261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:53.602365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
le70512
22.2%
par35256
11.1%
pouvoir35256
11.1%
pen35256
11.1%
marine35256
11.1%
liste35256
11.1%
soutenue35256
11.1%
prenez35256
11.1%

Most occurring characters

ValueCountFrequency (%)
E317304
17.6%
282048
15.7%
P141024
7.8%
R141024
7.8%
N141024
7.8%
L105768
 
5.9%
O105768
 
5.9%
U105768
 
5.9%
I105768
 
5.9%
S70512
 
3.9%
Other values (6)282048
15.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1480752
82.4%
Space Separator282048
 
15.7%
Other Punctuation35256
 
2.0%

Most frequent character per category

ValueCountFrequency (%)
E317304
21.4%
P141024
9.5%
R141024
9.5%
N141024
9.5%
L105768
 
7.1%
O105768
 
7.1%
U105768
 
7.1%
I105768
 
7.1%
S70512
 
4.8%
T70512
 
4.8%
Other values (4)176280
11.9%
ValueCountFrequency (%)
282048
100.0%
ValueCountFrequency (%)
,35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1480752
82.4%
Common317304
 
17.6%

Most frequent character per script

ValueCountFrequency (%)
E317304
21.4%
P141024
9.5%
R141024
9.5%
N141024
9.5%
L105768
 
7.1%
O105768
 
7.1%
U105768
 
7.1%
I105768
 
7.1%
S70512
 
4.8%
T70512
 
4.8%
Other values (4)176280
11.9%
ValueCountFrequency (%)
282048
88.9%
,35256
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1798056
100.0%

Most frequent character per block

ValueCountFrequency (%)
E317304
17.6%
282048
15.7%
P141024
7.8%
R141024
7.8%
N141024
7.8%
L105768
 
5.9%
O105768
 
5.9%
U105768
 
5.9%
I105768
 
5.9%
S70512
 
3.9%
Other values (6)282048
15.7%

Unnamed: 175
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
BARDELLA Jordan
35256 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters528840
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBARDELLA Jordan
2nd rowBARDELLA Jordan
3rd rowBARDELLA Jordan
4th rowBARDELLA Jordan
5th rowBARDELLA Jordan
ValueCountFrequency (%)
BARDELLA Jordan35256
100.0%
2021-02-18T22:26:53.734161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:53.787468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
bardella35256
50.0%
jordan35256
50.0%

Most occurring characters

ValueCountFrequency (%)
A70512
13.3%
L70512
13.3%
B35256
 
6.7%
R35256
 
6.7%
D35256
 
6.7%
E35256
 
6.7%
35256
 
6.7%
J35256
 
6.7%
o35256
 
6.7%
r35256
 
6.7%
Other values (3)105768
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter317304
60.0%
Lowercase Letter176280
33.3%
Space Separator35256
 
6.7%

Most frequent character per category

ValueCountFrequency (%)
A70512
22.2%
L70512
22.2%
B35256
11.1%
R35256
11.1%
D35256
11.1%
E35256
11.1%
J35256
11.1%
ValueCountFrequency (%)
o35256
20.0%
r35256
20.0%
d35256
20.0%
a35256
20.0%
n35256
20.0%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin493584
93.3%
Common35256
 
6.7%

Most frequent character per script

ValueCountFrequency (%)
A70512
14.3%
L70512
14.3%
B35256
7.1%
R35256
7.1%
D35256
7.1%
E35256
7.1%
J35256
7.1%
o35256
7.1%
r35256
7.1%
d35256
7.1%
Other values (2)70512
14.3%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII528840
100.0%

Most frequent character per block

ValueCountFrequency (%)
A70512
13.3%
L70512
13.3%
B35256
 
6.7%
R35256
 
6.7%
D35256
 
6.7%
E35256
 
6.7%
35256
 
6.7%
J35256
 
6.7%
o35256
 
6.7%
r35256
 
6.7%
Other values (3)105768
20.0%

Unnamed: 176
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct1516
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149.9585602
Minimum0
Maximum55974
Zeros86
Zeros (%)0.2%
Memory size275.6 KiB
2021-02-18T22:26:53.849052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q124
median53
Q3123
95-th percentile543
Maximum55974
Range55974
Interquartile range (IQR)99

Descriptive statistics

Standard deviation597.3134149
Coefficient of variation (CV)3.983189848
Kurtosis4209.343156
Mean149.9585602
Median Absolute Deviation (MAD)36
Skewness50.78746086
Sum5286939
Variance356783.3156
MonotocityNot monotonic
2021-02-18T22:26:53.951256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13503
 
1.4%
20459
 
1.3%
21456
 
1.3%
15451
 
1.3%
16446
 
1.3%
17442
 
1.3%
19439
 
1.2%
18437
 
1.2%
23436
 
1.2%
25431
 
1.2%
Other values (1506)30756
87.2%
ValueCountFrequency (%)
086
 
0.2%
1108
0.3%
2164
0.5%
3202
0.6%
4235
0.7%
ValueCountFrequency (%)
559741
< 0.1%
538291
< 0.1%
280141
< 0.1%
155511
< 0.1%
148701
< 0.1%

Unnamed: 177
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3014
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.93134105
Minimum0
Maximum58.33
Zeros86
Zeros (%)0.2%
Memory size275.6 KiB
2021-02-18T22:26:54.061540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.58
Q111.05
median14.43
Q318.29
95-th percentile25
Maximum58.33
Range58.33
Interquartile range (IQR)7.24

Descriptive statistics

Standard deviation5.744817959
Coefficient of variation (CV)0.3847489613
Kurtosis1.291157312
Mean14.93134105
Median Absolute Deviation (MAD)3.59
Skewness0.5968092382
Sum526419.36
Variance33.00293338
MonotocityNot monotonic
2021-02-18T22:26:54.166583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.67150
 
0.4%
20142
 
0.4%
12.5131
 
0.4%
14.29129
 
0.4%
10107
 
0.3%
11.11103
 
0.3%
2594
 
0.3%
086
 
0.2%
18.1880
 
0.2%
22.2271
 
0.2%
Other values (3004)34163
96.9%
ValueCountFrequency (%)
086
0.2%
0.111
 
< 0.1%
0.141
 
< 0.1%
0.21
 
< 0.1%
0.221
 
< 0.1%
ValueCountFrequency (%)
58.331
< 0.1%
54.841
< 0.1%
54.171
< 0.1%
51.351
< 0.1%
48.781
< 0.1%

Unnamed: 178
Real number (ℝ≥0)

HIGH CORRELATION

Distinct4446
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.99607613
Minimum0
Maximum100
Zeros86
Zeros (%)0.2%
Memory size275.6 KiB
2021-02-18T22:26:54.277596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.73
Q121.05
median27.48
Q334.43
95-th percentile45.1025
Maximum100
Range100
Interquartile range (IQR)13.38

Descriptive statistics

Standard deviation10.0026435
Coefficient of variation (CV)0.3572873373
Kurtosis0.4005407773
Mean27.99607613
Median Absolute Deviation (MAD)6.67
Skewness0.3269114578
Sum987029.66
Variance100.0528769
MonotocityNot monotonic
2021-02-18T22:26:54.384705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.33289
 
0.8%
25230
 
0.7%
20188
 
0.5%
28.57169
 
0.5%
22.22112
 
0.3%
40106
 
0.3%
16.67105
 
0.3%
27.27104
 
0.3%
23.08100
 
0.3%
3099
 
0.3%
Other values (4436)33754
95.7%
ValueCountFrequency (%)
086
0.2%
0.831
 
< 0.1%
1.171
 
< 0.1%
1.371
 
< 0.1%
1.451
 
< 0.1%
ValueCountFrequency (%)
1001
< 0.1%
851
< 0.1%
78.951
< 0.1%
78.691
< 0.1%
752
< 0.1%

Unnamed: 179
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
24
35256 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters70512
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row24
2nd row24
3rd row24
4th row24
5th row24
ValueCountFrequency (%)
2435256
100.0%
2021-02-18T22:26:55.499188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:55.556590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2435256
100.0%

Most occurring characters

ValueCountFrequency (%)
235256
50.0%
435256
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number70512
100.0%

Most frequent character per category

ValueCountFrequency (%)
235256
50.0%
435256
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common70512
100.0%

Most frequent character per script

ValueCountFrequency (%)
235256
50.0%
435256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70512
100.0%

Most frequent character per block

ValueCountFrequency (%)
235256
50.0%
435256
50.0%

Unnamed: 180
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
NEUTRE ET ACTIF
35256 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters528840
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNEUTRE ET ACTIF
2nd rowNEUTRE ET ACTIF
3rd rowNEUTRE ET ACTIF
4th rowNEUTRE ET ACTIF
5th rowNEUTRE ET ACTIF
ValueCountFrequency (%)
NEUTRE ET ACTIF35256
100.0%
2021-02-18T22:26:55.676713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:55.725840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
actif35256
33.3%
et35256
33.3%
neutre35256
33.3%

Most occurring characters

ValueCountFrequency (%)
E105768
20.0%
T105768
20.0%
70512
13.3%
N35256
 
6.7%
U35256
 
6.7%
R35256
 
6.7%
A35256
 
6.7%
C35256
 
6.7%
I35256
 
6.7%
F35256
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter458328
86.7%
Space Separator70512
 
13.3%

Most frequent character per category

ValueCountFrequency (%)
E105768
23.1%
T105768
23.1%
N35256
 
7.7%
U35256
 
7.7%
R35256
 
7.7%
A35256
 
7.7%
C35256
 
7.7%
I35256
 
7.7%
F35256
 
7.7%
ValueCountFrequency (%)
70512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin458328
86.7%
Common70512
 
13.3%

Most frequent character per script

ValueCountFrequency (%)
E105768
23.1%
T105768
23.1%
N35256
 
7.7%
U35256
 
7.7%
R35256
 
7.7%
A35256
 
7.7%
C35256
 
7.7%
I35256
 
7.7%
F35256
 
7.7%
ValueCountFrequency (%)
70512
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII528840
100.0%

Most frequent character per block

ValueCountFrequency (%)
E105768
20.0%
T105768
20.0%
70512
13.3%
N35256
 
6.7%
U35256
 
6.7%
R35256
 
6.7%
A35256
 
6.7%
C35256
 
6.7%
I35256
 
6.7%
F35256
 
6.7%

Unnamed: 181
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
NEUTRE ET ACTIF
35256 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters528840
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNEUTRE ET ACTIF
2nd rowNEUTRE ET ACTIF
3rd rowNEUTRE ET ACTIF
4th rowNEUTRE ET ACTIF
5th rowNEUTRE ET ACTIF
ValueCountFrequency (%)
NEUTRE ET ACTIF35256
100.0%
2021-02-18T22:26:55.846690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:55.895564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
actif35256
33.3%
et35256
33.3%
neutre35256
33.3%

Most occurring characters

ValueCountFrequency (%)
E105768
20.0%
T105768
20.0%
70512
13.3%
N35256
 
6.7%
U35256
 
6.7%
R35256
 
6.7%
A35256
 
6.7%
C35256
 
6.7%
I35256
 
6.7%
F35256
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter458328
86.7%
Space Separator70512
 
13.3%

Most frequent character per category

ValueCountFrequency (%)
E105768
23.1%
T105768
23.1%
N35256
 
7.7%
U35256
 
7.7%
R35256
 
7.7%
A35256
 
7.7%
C35256
 
7.7%
I35256
 
7.7%
F35256
 
7.7%
ValueCountFrequency (%)
70512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin458328
86.7%
Common70512
 
13.3%

Most frequent character per script

ValueCountFrequency (%)
E105768
23.1%
T105768
23.1%
N35256
 
7.7%
U35256
 
7.7%
R35256
 
7.7%
A35256
 
7.7%
C35256
 
7.7%
I35256
 
7.7%
F35256
 
7.7%
ValueCountFrequency (%)
70512
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII528840
100.0%

Most frequent character per block

ValueCountFrequency (%)
E105768
20.0%
T105768
20.0%
70512
13.3%
N35256
 
6.7%
U35256
 
6.7%
R35256
 
6.7%
A35256
 
6.7%
C35256
 
6.7%
I35256
 
6.7%
F35256
 
6.7%

Unnamed: 182
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
CORBET Cathy Denise Ginette
35256 

Length

Max length27
Median length27
Mean length27
Min length27

Characters and Unicode

Total characters951912
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCORBET Cathy Denise Ginette
2nd rowCORBET Cathy Denise Ginette
3rd rowCORBET Cathy Denise Ginette
4th rowCORBET Cathy Denise Ginette
5th rowCORBET Cathy Denise Ginette
ValueCountFrequency (%)
CORBET Cathy Denise Ginette35256
100.0%
2021-02-18T22:26:56.016121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:56.065570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
cathy35256
25.0%
corbet35256
25.0%
ginette35256
25.0%
denise35256
25.0%

Most occurring characters

ValueCountFrequency (%)
e141024
14.8%
105768
11.1%
t105768
11.1%
C70512
 
7.4%
n70512
 
7.4%
i70512
 
7.4%
O35256
 
3.7%
R35256
 
3.7%
B35256
 
3.7%
E35256
 
3.7%
Other values (7)246792
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter528840
55.6%
Uppercase Letter317304
33.3%
Space Separator105768
 
11.1%

Most frequent character per category

ValueCountFrequency (%)
C70512
22.2%
O35256
11.1%
R35256
11.1%
B35256
11.1%
E35256
11.1%
T35256
11.1%
D35256
11.1%
G35256
11.1%
ValueCountFrequency (%)
e141024
26.7%
t105768
20.0%
n70512
13.3%
i70512
13.3%
a35256
 
6.7%
h35256
 
6.7%
y35256
 
6.7%
s35256
 
6.7%
ValueCountFrequency (%)
105768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin846144
88.9%
Common105768
 
11.1%

Most frequent character per script

ValueCountFrequency (%)
e141024
16.7%
t105768
12.5%
C70512
 
8.3%
n70512
 
8.3%
i70512
 
8.3%
O35256
 
4.2%
R35256
 
4.2%
B35256
 
4.2%
E35256
 
4.2%
T35256
 
4.2%
Other values (6)211536
25.0%
ValueCountFrequency (%)
105768
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII951912
100.0%

Most frequent character per block

ValueCountFrequency (%)
e141024
14.8%
105768
11.1%
t105768
11.1%
C70512
 
7.4%
n70512
 
7.4%
i70512
 
7.4%
O35256
 
3.7%
R35256
 
3.7%
B35256
 
3.7%
E35256
 
3.7%
Other values (7)246792
25.9%

Unnamed: 183
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03746879964
Minimum0
Maximum56
Zeros34939
Zeros (%)99.1%
Memory size275.6 KiB
2021-02-18T22:26:56.114180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum56
Range56
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7680232417
Coefficient of variation (CV)20.49767404
Kurtosis2136.088943
Mean0.03746879964
Median Absolute Deviation (MAD)0
Skewness39.53230287
Sum1321
Variance0.5898596998
MonotocityNot monotonic
2021-02-18T22:26:56.193211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
034939
99.1%
1183
 
0.5%
239
 
0.1%
314
 
< 0.1%
410
 
< 0.1%
87
 
< 0.1%
56
 
< 0.1%
136
 
< 0.1%
115
 
< 0.1%
65
 
< 0.1%
Other values (19)42
 
0.1%
ValueCountFrequency (%)
034939
99.1%
1183
 
0.5%
239
 
0.1%
314
 
< 0.1%
410
 
< 0.1%
ValueCountFrequency (%)
561
< 0.1%
551
< 0.1%
401
< 0.1%
341
< 0.1%
261
< 0.1%

Unnamed: 184
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct62
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001305877014
Minimum0
Maximum1.59
Zeros34970
Zeros (%)99.2%
Memory size275.6 KiB
2021-02-18T22:26:56.287925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1.59
Range1.59
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0264801124
Coefficient of variation (CV)20.27764646
Kurtosis1274.093481
Mean0.001305877014
Median Absolute Deviation (MAD)0
Skewness32.32300812
Sum46.04
Variance0.0007011963529
MonotocityNot monotonic
2021-02-18T22:26:56.390388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034970
99.2%
0.0154
 
0.2%
0.0228
 
0.1%
0.0320
 
0.1%
0.0418
 
0.1%
0.0615
 
< 0.1%
0.0714
 
< 0.1%
0.0513
 
< 0.1%
0.18
 
< 0.1%
0.097
 
< 0.1%
Other values (52)109
 
0.3%
ValueCountFrequency (%)
034970
99.2%
0.0154
 
0.2%
0.0228
 
0.1%
0.0320
 
0.1%
0.0418
 
0.1%
ValueCountFrequency (%)
1.591
< 0.1%
1.221
< 0.1%
1.172
< 0.1%
1.091
< 0.1%
1.071
< 0.1%

Unnamed: 185
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct85
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.002528647606
Minimum0
Maximum3.23
Zeros34949
Zeros (%)99.1%
Memory size275.6 KiB
2021-02-18T22:26:56.499032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3.23
Range3.23
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.05026512352
Coefficient of variation (CV)19.87826354
Kurtosis1310.736343
Mean0.002528647606
Median Absolute Deviation (MAD)0
Skewness32.36510331
Sum89.15
Variance0.002526582643
MonotocityNot monotonic
2021-02-18T22:26:56.598570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034949
99.1%
0.0129
 
0.1%
0.0228
 
0.1%
0.0327
 
0.1%
0.0714
 
< 0.1%
0.1312
 
< 0.1%
0.0412
 
< 0.1%
0.0511
 
< 0.1%
0.069
 
< 0.1%
0.098
 
< 0.1%
Other values (75)157
 
0.4%
ValueCountFrequency (%)
034949
99.1%
0.0129
 
0.1%
0.0228
 
0.1%
0.0327
 
0.1%
0.0412
 
< 0.1%
ValueCountFrequency (%)
3.231
< 0.1%
2.22
< 0.1%
2.041
< 0.1%
1.991
< 0.1%
1.961
< 0.1%

Unnamed: 186
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
25
35256 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters70512
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row25
2nd row25
3rd row25
4th row25
5th row25
ValueCountFrequency (%)
2535256
100.0%
2021-02-18T22:26:56.765319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:56.813984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2535256
100.0%

Most occurring characters

ValueCountFrequency (%)
235256
50.0%
535256
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number70512
100.0%

Most frequent character per category

ValueCountFrequency (%)
235256
50.0%
535256
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common70512
100.0%

Most frequent character per script

ValueCountFrequency (%)
235256
50.0%
535256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70512
100.0%

Most frequent character per block

ValueCountFrequency (%)
235256
50.0%
535256
50.0%

Unnamed: 187
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
RÉVOLUTIONNAIRE
35256 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters528840
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRÉVOLUTIONNAIRE
2nd rowRÉVOLUTIONNAIRE
3rd rowRÉVOLUTIONNAIRE
4th rowRÉVOLUTIONNAIRE
5th rowRÉVOLUTIONNAIRE
ValueCountFrequency (%)
RÉVOLUTIONNAIRE35256
100.0%
2021-02-18T22:26:56.934281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:56.983455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
révolutionnaire35256
100.0%

Most occurring characters

ValueCountFrequency (%)
R70512
13.3%
O70512
13.3%
I70512
13.3%
N70512
13.3%
É35256
6.7%
V35256
6.7%
L35256
6.7%
U35256
6.7%
T35256
6.7%
A35256
6.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter528840
100.0%

Most frequent character per category

ValueCountFrequency (%)
R70512
13.3%
O70512
13.3%
I70512
13.3%
N70512
13.3%
É35256
6.7%
V35256
6.7%
L35256
6.7%
U35256
6.7%
T35256
6.7%
A35256
6.7%

Most occurring scripts

ValueCountFrequency (%)
Latin528840
100.0%

Most frequent character per script

ValueCountFrequency (%)
R70512
13.3%
O70512
13.3%
I70512
13.3%
N70512
13.3%
É35256
6.7%
V35256
6.7%
L35256
6.7%
U35256
6.7%
T35256
6.7%
A35256
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII493584
93.3%
None35256
 
6.7%

Most frequent character per block

ValueCountFrequency (%)
R70512
14.3%
O70512
14.3%
I70512
14.3%
N70512
14.3%
V35256
7.1%
L35256
7.1%
U35256
7.1%
T35256
7.1%
A35256
7.1%
E35256
7.1%
ValueCountFrequency (%)
É35256
100.0%

Unnamed: 188
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
PARTI RÉVOLUTIONNAIRE COMMUNISTES
35256 

Length

Max length33
Median length33
Mean length33
Min length33

Characters and Unicode

Total characters1163448
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPARTI RÉVOLUTIONNAIRE COMMUNISTES
2nd rowPARTI RÉVOLUTIONNAIRE COMMUNISTES
3rd rowPARTI RÉVOLUTIONNAIRE COMMUNISTES
4th rowPARTI RÉVOLUTIONNAIRE COMMUNISTES
5th rowPARTI RÉVOLUTIONNAIRE COMMUNISTES
ValueCountFrequency (%)
PARTI RÉVOLUTIONNAIRE COMMUNISTES35256
100.0%
2021-02-18T22:26:57.104270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:57.153701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
parti35256
33.3%
communistes35256
33.3%
révolutionnaire35256
33.3%

Most occurring characters

ValueCountFrequency (%)
I141024
12.1%
R105768
9.1%
T105768
9.1%
O105768
9.1%
N105768
9.1%
A70512
 
6.1%
70512
 
6.1%
U70512
 
6.1%
E70512
 
6.1%
M70512
 
6.1%
Other values (6)246792
21.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1092936
93.9%
Space Separator70512
 
6.1%

Most frequent character per category

ValueCountFrequency (%)
I141024
12.9%
R105768
9.7%
T105768
9.7%
O105768
9.7%
N105768
9.7%
A70512
 
6.5%
U70512
 
6.5%
E70512
 
6.5%
M70512
 
6.5%
S70512
 
6.5%
Other values (5)176280
16.1%
ValueCountFrequency (%)
70512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1092936
93.9%
Common70512
 
6.1%

Most frequent character per script

ValueCountFrequency (%)
I141024
12.9%
R105768
9.7%
T105768
9.7%
O105768
9.7%
N105768
9.7%
A70512
 
6.5%
U70512
 
6.5%
E70512
 
6.5%
M70512
 
6.5%
S70512
 
6.5%
Other values (5)176280
16.1%
ValueCountFrequency (%)
70512
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1128192
97.0%
None35256
 
3.0%

Most frequent character per block

ValueCountFrequency (%)
I141024
12.5%
R105768
9.4%
T105768
9.4%
O105768
9.4%
N105768
9.4%
A70512
 
6.2%
70512
 
6.2%
U70512
 
6.2%
E70512
 
6.2%
M70512
 
6.2%
Other values (5)211536
18.8%
ValueCountFrequency (%)
É35256
100.0%

Unnamed: 189
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
SANCHEZ Antonio
35256 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters528840
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSANCHEZ Antonio
2nd rowSANCHEZ Antonio
3rd rowSANCHEZ Antonio
4th rowSANCHEZ Antonio
5th rowSANCHEZ Antonio
ValueCountFrequency (%)
SANCHEZ Antonio35256
100.0%
2021-02-18T22:26:57.277224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:57.329432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
sanchez35256
50.0%
antonio35256
50.0%

Most occurring characters

ValueCountFrequency (%)
A70512
13.3%
n70512
13.3%
o70512
13.3%
S35256
6.7%
N35256
6.7%
C35256
6.7%
H35256
6.7%
E35256
6.7%
Z35256
6.7%
35256
6.7%
Other values (2)70512
13.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter282048
53.3%
Lowercase Letter211536
40.0%
Space Separator35256
 
6.7%

Most frequent character per category

ValueCountFrequency (%)
A70512
25.0%
S35256
12.5%
N35256
12.5%
C35256
12.5%
H35256
12.5%
E35256
12.5%
Z35256
12.5%
ValueCountFrequency (%)
n70512
33.3%
o70512
33.3%
t35256
16.7%
i35256
16.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin493584
93.3%
Common35256
 
6.7%

Most frequent character per script

ValueCountFrequency (%)
A70512
14.3%
n70512
14.3%
o70512
14.3%
S35256
7.1%
N35256
7.1%
C35256
7.1%
H35256
7.1%
E35256
7.1%
Z35256
7.1%
t35256
7.1%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII528840
100.0%

Most frequent character per block

ValueCountFrequency (%)
A70512
13.3%
n70512
13.3%
o70512
13.3%
S35256
6.7%
N35256
6.7%
C35256
6.7%
H35256
6.7%
E35256
6.7%
Z35256
6.7%
35256
6.7%
Other values (2)70512
13.3%

Unnamed: 190
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04007828455
Minimum0
Maximum109
Zeros35027
Zeros (%)99.4%
Memory size275.6 KiB
2021-02-18T22:26:57.383184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum109
Range109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.032947095
Coefficient of variation (CV)25.77323623
Kurtosis4564.015937
Mean0.04007828455
Median Absolute Deviation (MAD)0
Skewness56.74456778
Sum1413
Variance1.066979702
MonotocityNot monotonic
2021-02-18T22:26:57.473111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
035027
99.4%
1102
 
0.3%
229
 
0.1%
413
 
< 0.1%
310
 
< 0.1%
510
 
< 0.1%
79
 
< 0.1%
88
 
< 0.1%
65
 
< 0.1%
95
 
< 0.1%
Other values (23)38
 
0.1%
ValueCountFrequency (%)
035027
99.4%
1102
 
0.3%
229
 
0.1%
310
 
< 0.1%
413
 
< 0.1%
ValueCountFrequency (%)
1091
< 0.1%
671
< 0.1%
521
< 0.1%
451
< 0.1%
431
< 0.1%

Unnamed: 191
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct46
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0009326072158
Minimum0
Maximum7.07
Zeros35048
Zeros (%)99.4%
Memory size275.6 KiB
2021-02-18T22:26:57.567167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7.07
Range7.07
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.04271847858
Coefficient of variation (CV)45.80543433
Kurtosis21462.47869
Mean0.0009326072158
Median Absolute Deviation (MAD)0
Skewness134.3318034
Sum32.88
Variance0.001824868412
MonotocityNot monotonic
2021-02-18T22:26:57.666713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
035048
99.4%
0.0132
 
0.1%
0.0228
 
0.1%
0.0420
 
0.1%
0.0319
 
0.1%
0.0614
 
< 0.1%
0.0511
 
< 0.1%
0.078
 
< 0.1%
0.098
 
< 0.1%
0.17
 
< 0.1%
Other values (36)61
 
0.2%
ValueCountFrequency (%)
035048
99.4%
0.0132
 
0.1%
0.0228
 
0.1%
0.0319
 
0.1%
0.0420
 
0.1%
ValueCountFrequency (%)
7.071
< 0.1%
1.891
< 0.1%
1.391
< 0.1%
1.251
< 0.1%
1.141
< 0.1%

Unnamed: 192
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct66
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001822952122
Minimum0
Maximum12.96
Zeros35033
Zeros (%)99.4%
Memory size275.6 KiB
2021-02-18T22:26:57.769082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum12.96
Range12.96
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.07885644506
Coefficient of variation (CV)43.25755138
Kurtosis20925.05889
Mean0.001822952122
Median Absolute Deviation (MAD)0
Skewness132.2116957
Sum64.27
Variance0.006218338927
MonotocityNot monotonic
2021-02-18T22:26:57.876564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
035033
99.4%
0.0125
 
0.1%
0.0419
 
0.1%
0.0214
 
< 0.1%
0.0312
 
< 0.1%
0.0711
 
< 0.1%
0.0511
 
< 0.1%
0.159
 
< 0.1%
0.089
 
< 0.1%
0.069
 
< 0.1%
Other values (56)104
 
0.3%
ValueCountFrequency (%)
035033
99.4%
0.0125
 
0.1%
0.0214
 
< 0.1%
0.0312
 
< 0.1%
0.0419
 
0.1%
ValueCountFrequency (%)
12.961
< 0.1%
41
< 0.1%
2.171
< 0.1%
21
< 0.1%
1.891
< 0.1%

Unnamed: 193
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
26
35256 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters70512
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26
2nd row26
3rd row26
4th row26
5th row26
ValueCountFrequency (%)
2635256
100.0%
2021-02-18T22:26:58.041833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:58.090269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2635256
100.0%

Most occurring characters

ValueCountFrequency (%)
235256
50.0%
635256
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number70512
100.0%

Most frequent character per category

ValueCountFrequency (%)
235256
50.0%
635256
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common70512
100.0%

Most frequent character per script

ValueCountFrequency (%)
235256
50.0%
635256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70512
100.0%

Most frequent character per block

ValueCountFrequency (%)
235256
50.0%
635256
50.0%

Unnamed: 194
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
ESPERANTO
35256 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters317304
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESPERANTO
2nd rowESPERANTO
3rd rowESPERANTO
4th rowESPERANTO
5th rowESPERANTO
ValueCountFrequency (%)
ESPERANTO35256
100.0%
2021-02-18T22:26:58.209749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:58.258354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
esperanto35256
100.0%

Most occurring characters

ValueCountFrequency (%)
E70512
22.2%
S35256
11.1%
P35256
11.1%
R35256
11.1%
A35256
11.1%
N35256
11.1%
T35256
11.1%
O35256
11.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter317304
100.0%

Most frequent character per category

ValueCountFrequency (%)
E70512
22.2%
S35256
11.1%
P35256
11.1%
R35256
11.1%
A35256
11.1%
N35256
11.1%
T35256
11.1%
O35256
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin317304
100.0%

Most frequent character per script

ValueCountFrequency (%)
E70512
22.2%
S35256
11.1%
P35256
11.1%
R35256
11.1%
A35256
11.1%
N35256
11.1%
T35256
11.1%
O35256
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII317304
100.0%

Most frequent character per block

ValueCountFrequency (%)
E70512
22.2%
S35256
11.1%
P35256
11.1%
R35256
11.1%
A35256
11.1%
N35256
11.1%
T35256
11.1%
O35256
11.1%

Unnamed: 195
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
ESPÉRANTO - LANGUE COMMUNE ÉQUITABLE POUR L'EUROPE
35256 

Length

Max length50
Median length50
Mean length50
Min length50

Characters and Unicode

Total characters1762800
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESPÉRANTO - LANGUE COMMUNE ÉQUITABLE POUR L'EUROPE
2nd rowESPÉRANTO - LANGUE COMMUNE ÉQUITABLE POUR L'EUROPE
3rd rowESPÉRANTO - LANGUE COMMUNE ÉQUITABLE POUR L'EUROPE
4th rowESPÉRANTO - LANGUE COMMUNE ÉQUITABLE POUR L'EUROPE
5th rowESPÉRANTO - LANGUE COMMUNE ÉQUITABLE POUR L'EUROPE
ValueCountFrequency (%)
ESPÉRANTO - LANGUE COMMUNE ÉQUITABLE POUR L'EUROPE35256
100.0%
2021-02-18T22:26:58.379057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:58.429050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
commune35256
14.3%
pour35256
14.3%
équitable35256
14.3%
l'europe35256
14.3%
langue35256
14.3%
espéranto35256
14.3%
35256
14.3%

Most occurring characters

ValueCountFrequency (%)
E211536
12.0%
211536
12.0%
U176280
10.0%
O141024
 
8.0%
P105768
 
6.0%
R105768
 
6.0%
A105768
 
6.0%
N105768
 
6.0%
L105768
 
6.0%
É70512
 
4.0%
Other values (10)423072
24.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1480752
84.0%
Space Separator211536
 
12.0%
Dash Punctuation35256
 
2.0%
Other Punctuation35256
 
2.0%

Most frequent character per category

ValueCountFrequency (%)
E211536
14.3%
U176280
11.9%
O141024
9.5%
P105768
 
7.1%
R105768
 
7.1%
A105768
 
7.1%
N105768
 
7.1%
L105768
 
7.1%
É70512
 
4.8%
T70512
 
4.8%
Other values (7)282048
19.0%
ValueCountFrequency (%)
211536
100.0%
ValueCountFrequency (%)
-35256
100.0%
ValueCountFrequency (%)
'35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1480752
84.0%
Common282048
 
16.0%

Most frequent character per script

ValueCountFrequency (%)
E211536
14.3%
U176280
11.9%
O141024
9.5%
P105768
 
7.1%
R105768
 
7.1%
A105768
 
7.1%
N105768
 
7.1%
L105768
 
7.1%
É70512
 
4.8%
T70512
 
4.8%
Other values (7)282048
19.0%
ValueCountFrequency (%)
211536
75.0%
-35256
 
12.5%
'35256
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1692288
96.0%
None70512
 
4.0%

Most frequent character per block

ValueCountFrequency (%)
E211536
12.5%
211536
12.5%
U176280
10.4%
O141024
 
8.3%
P105768
 
6.2%
R105768
 
6.2%
A105768
 
6.2%
N105768
 
6.2%
L105768
 
6.2%
T70512
 
4.2%
Other values (9)352560
20.8%
ValueCountFrequency (%)
É70512
100.0%

Unnamed: 196
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
DIEUMEGARD Pierre
35256 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters599352
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDIEUMEGARD Pierre
2nd rowDIEUMEGARD Pierre
3rd rowDIEUMEGARD Pierre
4th rowDIEUMEGARD Pierre
5th rowDIEUMEGARD Pierre
ValueCountFrequency (%)
DIEUMEGARD Pierre35256
100.0%
2021-02-18T22:26:58.552358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:58.601922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
pierre35256
50.0%
dieumegard35256
50.0%

Most occurring characters

ValueCountFrequency (%)
D70512
11.8%
E70512
11.8%
e70512
11.8%
r70512
11.8%
I35256
 
5.9%
U35256
 
5.9%
M35256
 
5.9%
G35256
 
5.9%
A35256
 
5.9%
R35256
 
5.9%
Other values (3)105768
17.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter387816
64.7%
Lowercase Letter176280
29.4%
Space Separator35256
 
5.9%

Most frequent character per category

ValueCountFrequency (%)
D70512
18.2%
E70512
18.2%
I35256
9.1%
U35256
9.1%
M35256
9.1%
G35256
9.1%
A35256
9.1%
R35256
9.1%
P35256
9.1%
ValueCountFrequency (%)
e70512
40.0%
r70512
40.0%
i35256
20.0%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin564096
94.1%
Common35256
 
5.9%

Most frequent character per script

ValueCountFrequency (%)
D70512
12.5%
E70512
12.5%
e70512
12.5%
r70512
12.5%
I35256
6.2%
U35256
6.2%
M35256
6.2%
G35256
6.2%
A35256
6.2%
R35256
6.2%
Other values (2)70512
12.5%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII599352
100.0%

Most frequent character per block

ValueCountFrequency (%)
D70512
11.8%
E70512
11.8%
e70512
11.8%
r70512
11.8%
I35256
 
5.9%
U35256
 
5.9%
M35256
 
5.9%
G35256
 
5.9%
A35256
 
5.9%
R35256
 
5.9%
Other values (3)105768
17.6%

Unnamed: 197
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct52
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5272010438
Minimum0
Maximum326
Zeros26822
Zeros (%)76.1%
Memory size275.6 KiB
2021-02-18T22:26:58.660918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum326
Range326
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.678636462
Coefficient of variation (CV)5.080863352
Kurtosis6518.912953
Mean0.5272010438
Median Absolute Deviation (MAD)0
Skewness60.67898278
Sum18587
Variance7.175093297
MonotocityNot monotonic
2021-02-18T22:26:58.759918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
026822
76.1%
15192
 
14.7%
21605
 
4.6%
3628
 
1.8%
4323
 
0.9%
5210
 
0.6%
6121
 
0.3%
778
 
0.2%
868
 
0.2%
933
 
0.1%
Other values (42)176
 
0.5%
ValueCountFrequency (%)
026822
76.1%
15192
 
14.7%
21605
 
4.6%
3628
 
1.8%
4323
 
0.9%
ValueCountFrequency (%)
3261
< 0.1%
1451
< 0.1%
801
< 0.1%
711
< 0.1%
691
< 0.1%

Unnamed: 198
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct186
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05192988428
Minimum0
Maximum13.46
Zeros26833
Zeros (%)76.1%
Memory size275.6 KiB
2021-02-18T22:26:58.862088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.29
Maximum13.46
Range13.46
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.197430616
Coefficient of variation (CV)3.801868977
Kurtosis743.5593338
Mean0.05192988428
Median Absolute Deviation (MAD)0
Skewness17.10861053
Sum1830.84
Variance0.03897884813
MonotocityNot monotonic
2021-02-18T22:26:58.969935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
026833
76.1%
0.04562
 
1.6%
0.03511
 
1.4%
0.05500
 
1.4%
0.06476
 
1.4%
0.02385
 
1.1%
0.07369
 
1.0%
0.08365
 
1.0%
0.09352
 
1.0%
0.11299
 
0.8%
Other values (176)4604
 
13.1%
ValueCountFrequency (%)
026833
76.1%
0.01123
 
0.3%
0.02385
 
1.1%
0.03511
 
1.4%
0.04562
 
1.6%
ValueCountFrequency (%)
13.461
< 0.1%
6.451
< 0.1%
5.561
< 0.1%
5.311
< 0.1%
5.131
< 0.1%

Unnamed: 199
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct247
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09698434309
Minimum0
Maximum22.58
Zeros26824
Zeros (%)76.1%
Memory size275.6 KiB
2021-02-18T22:26:59.076393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.55
Maximum22.58
Range22.58
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3528964044
Coefficient of variation (CV)3.638694589
Kurtosis634.885526
Mean0.09698434309
Median Absolute Deviation (MAD)0
Skewness15.93095732
Sum3419.28
Variance0.1245358722
MonotocityNot monotonic
2021-02-18T22:26:59.174588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
026824
76.1%
0.09296
 
0.8%
0.07280
 
0.8%
0.08272
 
0.8%
0.06266
 
0.8%
0.1251
 
0.7%
0.12240
 
0.7%
0.05234
 
0.7%
0.13226
 
0.6%
0.11224
 
0.6%
Other values (237)6143
 
17.4%
ValueCountFrequency (%)
026824
76.1%
0.0111
 
< 0.1%
0.0245
 
0.1%
0.03119
 
0.3%
0.04181
 
0.5%
ValueCountFrequency (%)
22.581
< 0.1%
12.961
< 0.1%
12.241
< 0.1%
101
< 0.1%
7.691
< 0.1%

Unnamed: 200
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
27
35256 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters70512
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row27
2nd row27
3rd row27
4th row27
5th row27
ValueCountFrequency (%)
2735256
100.0%
2021-02-18T22:26:59.341845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:59.392780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2735256
100.0%

Most occurring characters

ValueCountFrequency (%)
235256
50.0%
735256
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number70512
100.0%

Most frequent character per category

ValueCountFrequency (%)
235256
50.0%
735256
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common70512
100.0%

Most frequent character per script

ValueCountFrequency (%)
235256
50.0%
735256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70512
100.0%

Most frequent character per block

ValueCountFrequency (%)
235256
50.0%
735256
50.0%

Unnamed: 201
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
ÉVOLUTION CITOYENNE
35256 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters669864
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowÉVOLUTION CITOYENNE
2nd rowÉVOLUTION CITOYENNE
3rd rowÉVOLUTION CITOYENNE
4th rowÉVOLUTION CITOYENNE
5th rowÉVOLUTION CITOYENNE
ValueCountFrequency (%)
ÉVOLUTION CITOYENNE35256
100.0%
2021-02-18T22:26:59.519136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:59.572222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
citoyenne35256
50.0%
évolution35256
50.0%

Most occurring characters

ValueCountFrequency (%)
O105768
15.8%
N105768
15.8%
T70512
10.5%
I70512
10.5%
E70512
10.5%
É35256
 
5.3%
V35256
 
5.3%
L35256
 
5.3%
U35256
 
5.3%
35256
 
5.3%
Other values (2)70512
10.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter634608
94.7%
Space Separator35256
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
O105768
16.7%
N105768
16.7%
T70512
11.1%
I70512
11.1%
E70512
11.1%
É35256
 
5.6%
V35256
 
5.6%
L35256
 
5.6%
U35256
 
5.6%
C35256
 
5.6%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin634608
94.7%
Common35256
 
5.3%

Most frequent character per script

ValueCountFrequency (%)
O105768
16.7%
N105768
16.7%
T70512
11.1%
I70512
11.1%
E70512
11.1%
É35256
 
5.6%
V35256
 
5.6%
L35256
 
5.6%
U35256
 
5.6%
C35256
 
5.6%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII634608
94.7%
None35256
 
5.3%

Most frequent character per block

ValueCountFrequency (%)
É35256
100.0%
ValueCountFrequency (%)
O105768
16.7%
N105768
16.7%
T70512
11.1%
I70512
11.1%
E70512
11.1%
V35256
 
5.6%
L35256
 
5.6%
U35256
 
5.6%
35256
 
5.6%
C35256
 
5.6%

Unnamed: 202
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
ÉVOLUTION CITOYENNE
35256 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters669864
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowÉVOLUTION CITOYENNE
2nd rowÉVOLUTION CITOYENNE
3rd rowÉVOLUTION CITOYENNE
4th rowÉVOLUTION CITOYENNE
5th rowÉVOLUTION CITOYENNE
ValueCountFrequency (%)
ÉVOLUTION CITOYENNE35256
100.0%
2021-02-18T22:26:59.702119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:59.757462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
citoyenne35256
50.0%
évolution35256
50.0%

Most occurring characters

ValueCountFrequency (%)
O105768
15.8%
N105768
15.8%
T70512
10.5%
I70512
10.5%
E70512
10.5%
É35256
 
5.3%
V35256
 
5.3%
L35256
 
5.3%
U35256
 
5.3%
35256
 
5.3%
Other values (2)70512
10.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter634608
94.7%
Space Separator35256
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
O105768
16.7%
N105768
16.7%
T70512
11.1%
I70512
11.1%
E70512
11.1%
É35256
 
5.6%
V35256
 
5.6%
L35256
 
5.6%
U35256
 
5.6%
C35256
 
5.6%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin634608
94.7%
Common35256
 
5.3%

Most frequent character per script

ValueCountFrequency (%)
O105768
16.7%
N105768
16.7%
T70512
11.1%
I70512
11.1%
E70512
11.1%
É35256
 
5.6%
V35256
 
5.6%
L35256
 
5.6%
U35256
 
5.6%
C35256
 
5.6%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII634608
94.7%
None35256
 
5.3%

Most frequent character per block

ValueCountFrequency (%)
É35256
100.0%
ValueCountFrequency (%)
O105768
16.7%
N105768
16.7%
T70512
11.1%
I70512
11.1%
E70512
11.1%
V35256
 
5.6%
L35256
 
5.6%
U35256
 
5.6%
35256
 
5.6%
C35256
 
5.6%

Unnamed: 203
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
CHALENÇON Christophe
35256 

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters705120
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHALENÇON Christophe
2nd rowCHALENÇON Christophe
3rd rowCHALENÇON Christophe
4th rowCHALENÇON Christophe
5th rowCHALENÇON Christophe
ValueCountFrequency (%)
CHALENÇON Christophe35256
100.0%
2021-02-18T22:26:59.887998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:26:59.942313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
christophe35256
50.0%
chalençon35256
50.0%

Most occurring characters

ValueCountFrequency (%)
C70512
 
10.0%
N70512
 
10.0%
h70512
 
10.0%
H35256
 
5.0%
A35256
 
5.0%
L35256
 
5.0%
E35256
 
5.0%
Ç35256
 
5.0%
O35256
 
5.0%
35256
 
5.0%
Other values (7)246792
35.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter352560
50.0%
Lowercase Letter317304
45.0%
Space Separator35256
 
5.0%

Most frequent character per category

ValueCountFrequency (%)
C70512
20.0%
N70512
20.0%
H35256
10.0%
A35256
10.0%
L35256
10.0%
E35256
10.0%
Ç35256
10.0%
O35256
10.0%
ValueCountFrequency (%)
h70512
22.2%
r35256
11.1%
i35256
11.1%
s35256
11.1%
t35256
11.1%
o35256
11.1%
p35256
11.1%
e35256
11.1%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin669864
95.0%
Common35256
 
5.0%

Most frequent character per script

ValueCountFrequency (%)
C70512
 
10.5%
N70512
 
10.5%
h70512
 
10.5%
H35256
 
5.3%
A35256
 
5.3%
L35256
 
5.3%
E35256
 
5.3%
Ç35256
 
5.3%
O35256
 
5.3%
r35256
 
5.3%
Other values (6)211536
31.6%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII669864
95.0%
None35256
 
5.0%

Most frequent character per block

ValueCountFrequency (%)
C70512
 
10.5%
N70512
 
10.5%
h70512
 
10.5%
H35256
 
5.3%
A35256
 
5.3%
L35256
 
5.3%
E35256
 
5.3%
O35256
 
5.3%
35256
 
5.3%
r35256
 
5.3%
Other values (6)211536
31.6%
ValueCountFrequency (%)
Ç35256
100.0%

Unnamed: 204
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05845813479
Minimum0
Maximum36
Zeros34215
Zeros (%)97.0%
Memory size275.6 KiB
2021-02-18T22:26:59.992779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum36
Range36
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5639004959
Coefficient of variation (CV)9.646227987
Kurtosis1189.328453
Mean0.05845813479
Median Absolute Deviation (MAD)0
Skewness27.69953719
Sum2061
Variance0.3179837692
MonotocityNot monotonic
2021-02-18T22:27:00.078860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
034215
97.0%
1636
 
1.8%
2244
 
0.7%
371
 
0.2%
428
 
0.1%
515
 
< 0.1%
710
 
< 0.1%
610
 
< 0.1%
144
 
< 0.1%
124
 
< 0.1%
Other values (12)19
 
0.1%
ValueCountFrequency (%)
034215
97.0%
1636
 
1.8%
2244
 
0.7%
371
 
0.2%
428
 
0.1%
ValueCountFrequency (%)
361
< 0.1%
321
< 0.1%
241
< 0.1%
221
< 0.1%
211
< 0.1%

Unnamed: 205
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct111
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004939584752
Minimum0
Maximum4
Zeros34281
Zeros (%)97.2%
Memory size275.6 KiB
2021-02-18T22:27:00.183712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.06419002585
Coefficient of variation (CV)12.9950247
Kurtosis1207.190046
Mean0.004939584752
Median Absolute Deviation (MAD)0
Skewness28.6470902
Sum174.15
Variance0.004120359419
MonotocityNot monotonic
2021-02-18T22:27:00.296075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034281
97.2%
0.01155
 
0.4%
0.02111
 
0.3%
0.0399
 
0.3%
0.0466
 
0.2%
0.0551
 
0.1%
0.0635
 
0.1%
0.0732
 
0.1%
0.0829
 
0.1%
0.0927
 
0.1%
Other values (101)370
 
1.0%
ValueCountFrequency (%)
034281
97.2%
0.01155
 
0.4%
0.02111
 
0.3%
0.0399
 
0.3%
0.0466
 
0.2%
ValueCountFrequency (%)
41
< 0.1%
3.851
< 0.1%
2.861
< 0.1%
2.331
< 0.1%
2.111
< 0.1%

Unnamed: 206
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct151
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00925714772
Minimum0
Maximum6.67
Zeros34231
Zeros (%)97.1%
Memory size275.6 KiB
2021-02-18T22:27:00.415649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6.67
Range6.67
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1152915741
Coefficient of variation (CV)12.45433017
Kurtosis949.4971643
Mean0.00925714772
Median Absolute Deviation (MAD)0
Skewness25.80643204
Sum326.37
Variance0.01329214706
MonotocityNot monotonic
2021-02-18T22:27:00.523874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034231
97.1%
0.0196
 
0.3%
0.0373
 
0.2%
0.0266
 
0.2%
0.0565
 
0.2%
0.0652
 
0.1%
0.0442
 
0.1%
0.0839
 
0.1%
0.0737
 
0.1%
0.0932
 
0.1%
Other values (141)523
 
1.5%
ValueCountFrequency (%)
034231
97.1%
0.0196
 
0.3%
0.0266
 
0.2%
0.0373
 
0.2%
0.0442
 
0.1%
ValueCountFrequency (%)
6.671
< 0.1%
5.881
< 0.1%
5.261
< 0.1%
4.231
< 0.1%
3.641
< 0.1%

Unnamed: 207
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
28
35256 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters70512
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28
2nd row28
3rd row28
4th row28
5th row28
ValueCountFrequency (%)
2835256
100.0%
2021-02-18T22:27:00.704197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:00.756532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2835256
100.0%

Most occurring characters

ValueCountFrequency (%)
235256
50.0%
835256
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number70512
100.0%

Most frequent character per category

ValueCountFrequency (%)
235256
50.0%
835256
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common70512
100.0%

Most frequent character per script

ValueCountFrequency (%)
235256
50.0%
835256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70512
100.0%

Most frequent character per block

ValueCountFrequency (%)
235256
50.0%
835256
50.0%

Unnamed: 208
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
ALLIANCE JAUNE
35256 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters493584
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowALLIANCE JAUNE
2nd rowALLIANCE JAUNE
3rd rowALLIANCE JAUNE
4th rowALLIANCE JAUNE
5th rowALLIANCE JAUNE
ValueCountFrequency (%)
ALLIANCE JAUNE35256
100.0%
2021-02-18T22:27:00.885637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:00.938505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
jaune35256
50.0%
alliance35256
50.0%

Most occurring characters

ValueCountFrequency (%)
A105768
21.4%
L70512
14.3%
N70512
14.3%
E70512
14.3%
I35256
 
7.1%
C35256
 
7.1%
35256
 
7.1%
J35256
 
7.1%
U35256
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter458328
92.9%
Space Separator35256
 
7.1%

Most frequent character per category

ValueCountFrequency (%)
A105768
23.1%
L70512
15.4%
N70512
15.4%
E70512
15.4%
I35256
 
7.7%
C35256
 
7.7%
J35256
 
7.7%
U35256
 
7.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin458328
92.9%
Common35256
 
7.1%

Most frequent character per script

ValueCountFrequency (%)
A105768
23.1%
L70512
15.4%
N70512
15.4%
E70512
15.4%
I35256
 
7.7%
C35256
 
7.7%
J35256
 
7.7%
U35256
 
7.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII493584
100.0%

Most frequent character per block

ValueCountFrequency (%)
A105768
21.4%
L70512
14.3%
N70512
14.3%
E70512
14.3%
I35256
 
7.1%
C35256
 
7.1%
35256
 
7.1%
J35256
 
7.1%
U35256
 
7.1%

Unnamed: 209
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
ALLIANCE JAUNE, LA RÉVOLTE PAR LE VOTE
35256 

Length

Max length38
Median length38
Mean length38
Min length38

Characters and Unicode

Total characters1339728
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowALLIANCE JAUNE, LA RÉVOLTE PAR LE VOTE
2nd rowALLIANCE JAUNE, LA RÉVOLTE PAR LE VOTE
3rd rowALLIANCE JAUNE, LA RÉVOLTE PAR LE VOTE
4th rowALLIANCE JAUNE, LA RÉVOLTE PAR LE VOTE
5th rowALLIANCE JAUNE, LA RÉVOLTE PAR LE VOTE
ValueCountFrequency (%)
ALLIANCE JAUNE, LA RÉVOLTE PAR LE VOTE35256
100.0%
2021-02-18T22:27:01.067988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:01.121311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
par35256
14.3%
alliance35256
14.3%
vote35256
14.3%
jaune35256
14.3%
la35256
14.3%
révolte35256
14.3%
le35256
14.3%

Most occurring characters

ValueCountFrequency (%)
211536
15.8%
A176280
13.2%
L176280
13.2%
E176280
13.2%
N70512
 
5.3%
R70512
 
5.3%
V70512
 
5.3%
O70512
 
5.3%
T70512
 
5.3%
I35256
 
2.6%
Other values (6)211536
15.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1092936
81.6%
Space Separator211536
 
15.8%
Other Punctuation35256
 
2.6%

Most frequent character per category

ValueCountFrequency (%)
A176280
16.1%
L176280
16.1%
E176280
16.1%
N70512
 
6.5%
R70512
 
6.5%
V70512
 
6.5%
O70512
 
6.5%
T70512
 
6.5%
I35256
 
3.2%
C35256
 
3.2%
Other values (4)141024
12.9%
ValueCountFrequency (%)
211536
100.0%
ValueCountFrequency (%)
,35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1092936
81.6%
Common246792
 
18.4%

Most frequent character per script

ValueCountFrequency (%)
A176280
16.1%
L176280
16.1%
E176280
16.1%
N70512
 
6.5%
R70512
 
6.5%
V70512
 
6.5%
O70512
 
6.5%
T70512
 
6.5%
I35256
 
3.2%
C35256
 
3.2%
Other values (4)141024
12.9%
ValueCountFrequency (%)
211536
85.7%
,35256
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1304472
97.4%
None35256
 
2.6%

Most frequent character per block

ValueCountFrequency (%)
211536
16.2%
A176280
13.5%
L176280
13.5%
E176280
13.5%
N70512
 
5.4%
R70512
 
5.4%
V70512
 
5.4%
O70512
 
5.4%
T70512
 
5.4%
I35256
 
2.7%
Other values (5)176280
13.5%
ValueCountFrequency (%)
É35256
100.0%

Unnamed: 210
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
LALANNE Francis
35256 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters528840
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLALANNE Francis
2nd rowLALANNE Francis
3rd rowLALANNE Francis
4th rowLALANNE Francis
5th rowLALANNE Francis
ValueCountFrequency (%)
LALANNE Francis35256
100.0%
2021-02-18T22:27:01.252263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:01.305552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
francis35256
50.0%
lalanne35256
50.0%

Most occurring characters

ValueCountFrequency (%)
L70512
13.3%
A70512
13.3%
N70512
13.3%
E35256
6.7%
35256
6.7%
F35256
6.7%
r35256
6.7%
a35256
6.7%
n35256
6.7%
c35256
6.7%
Other values (2)70512
13.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter282048
53.3%
Lowercase Letter211536
40.0%
Space Separator35256
 
6.7%

Most frequent character per category

ValueCountFrequency (%)
r35256
16.7%
a35256
16.7%
n35256
16.7%
c35256
16.7%
i35256
16.7%
s35256
16.7%
ValueCountFrequency (%)
L70512
25.0%
A70512
25.0%
N70512
25.0%
E35256
12.5%
F35256
12.5%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin493584
93.3%
Common35256
 
6.7%

Most frequent character per script

ValueCountFrequency (%)
L70512
14.3%
A70512
14.3%
N70512
14.3%
E35256
7.1%
F35256
7.1%
r35256
7.1%
a35256
7.1%
n35256
7.1%
c35256
7.1%
i35256
7.1%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII528840
100.0%

Most frequent character per block

ValueCountFrequency (%)
L70512
13.3%
A70512
13.3%
N70512
13.3%
E35256
6.7%
35256
6.7%
F35256
6.7%
r35256
6.7%
a35256
6.7%
n35256
6.7%
c35256
6.7%
Other values (2)70512
13.3%

Unnamed: 211
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct141
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.437968005
Minimum0
Maximum1088
Zeros13110
Zeros (%)37.2%
Memory size275.6 KiB
2021-02-18T22:27:01.370779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile13
Maximum1088
Range1088
Interquartile range (IQR)3

Descriptive statistics

Standard deviation12.69204575
Coefficient of variation (CV)3.691728874
Kurtosis2784.670081
Mean3.437968005
Median Absolute Deviation (MAD)1
Skewness38.97200489
Sum121209
Variance161.0880254
MonotocityNot monotonic
2021-02-18T22:27:01.481734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013110
37.2%
17273
20.6%
24292
 
12.2%
32670
 
7.6%
41760
 
5.0%
51188
 
3.4%
6807
 
2.3%
7595
 
1.7%
8478
 
1.4%
9361
 
1.0%
Other values (131)2722
 
7.7%
ValueCountFrequency (%)
013110
37.2%
17273
20.6%
24292
 
12.2%
32670
 
7.6%
41760
 
5.0%
ValueCountFrequency (%)
10881
< 0.1%
10081
< 0.1%
5101
< 0.1%
3901
< 0.1%
3151
< 0.1%

Unnamed: 212
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct348
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3435219537
Minimum0
Maximum10.17
Zeros13114
Zeros (%)37.2%
Memory size275.6 KiB
2021-02-18T22:27:01.593475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.22
Q30.49
95-th percentile1.18
Maximum10.17
Range10.17
Interquartile range (IQR)0.49

Descriptive statistics

Standard deviation0.4964535003
Coefficient of variation (CV)1.445187112
Kurtosis34.44558277
Mean0.3435219537
Median Absolute Deviation (MAD)0.22
Skewness4.123169169
Sum12111.21
Variance0.246466078
MonotocityNot monotonic
2021-02-18T22:27:01.706414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013114
37.2%
0.22418
 
1.2%
0.27412
 
1.2%
0.23400
 
1.1%
0.24390
 
1.1%
0.21389
 
1.1%
0.26386
 
1.1%
0.31385
 
1.1%
0.25385
 
1.1%
0.29376
 
1.1%
Other values (338)18601
52.8%
ValueCountFrequency (%)
013114
37.2%
0.0127
 
0.1%
0.0226
 
0.1%
0.0331
 
0.1%
0.0435
 
0.1%
ValueCountFrequency (%)
10.171
< 0.1%
8.71
< 0.1%
8.331
< 0.1%
7.691
< 0.1%
7.551
< 0.1%

Unnamed: 213
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct466
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.642840084
Minimum0
Maximum17.14
Zeros13110
Zeros (%)37.2%
Memory size275.6 KiB
2021-02-18T22:27:01.817546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.43
Q30.93
95-th percentile2.14
Maximum17.14
Range17.14
Interquartile range (IQR)0.93

Descriptive statistics

Standard deviation0.8837501436
Coefficient of variation (CV)1.374758926
Kurtosis27.03980727
Mean0.642840084
Median Absolute Deviation (MAD)0.43
Skewness3.616164435
Sum22663.97
Variance0.7810143163
MonotocityNot monotonic
2021-02-18T22:27:01.926737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013110
37.2%
0.56235
 
0.7%
0.53229
 
0.6%
0.55226
 
0.6%
0.58217
 
0.6%
0.52213
 
0.6%
0.51212
 
0.6%
0.63210
 
0.6%
0.47209
 
0.6%
0.54208
 
0.6%
Other values (456)20187
57.3%
ValueCountFrequency (%)
013110
37.2%
0.011
 
< 0.1%
0.021
 
< 0.1%
0.036
 
< 0.1%
0.043
 
< 0.1%
ValueCountFrequency (%)
17.141
< 0.1%
16.671
< 0.1%
12.91
< 0.1%
12.51
< 0.1%
11.762
< 0.1%

Unnamed: 214
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
29
35256 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters70512
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row29
2nd row29
3rd row29
4th row29
5th row29
ValueCountFrequency (%)
2935256
100.0%
2021-02-18T22:27:02.108544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:02.160631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2935256
100.0%

Most occurring characters

ValueCountFrequency (%)
235256
50.0%
935256
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number70512
100.0%

Most frequent character per category

ValueCountFrequency (%)
235256
50.0%
935256
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common70512
100.0%

Most frequent character per script

ValueCountFrequency (%)
235256
50.0%
935256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70512
100.0%

Most frequent character per block

ValueCountFrequency (%)
235256
50.0%
935256
50.0%

Unnamed: 215
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
UNION DROITE-CENTRE
35256 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters669864
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUNION DROITE-CENTRE
2nd rowUNION DROITE-CENTRE
3rd rowUNION DROITE-CENTRE
4th rowUNION DROITE-CENTRE
5th rowUNION DROITE-CENTRE
ValueCountFrequency (%)
UNION DROITE-CENTRE35256
100.0%
2021-02-18T22:27:02.289779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:02.343906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
droite-centre35256
50.0%
union35256
50.0%

Most occurring characters

ValueCountFrequency (%)
N105768
15.8%
E105768
15.8%
I70512
10.5%
O70512
10.5%
R70512
10.5%
T70512
10.5%
U35256
 
5.3%
35256
 
5.3%
D35256
 
5.3%
-35256
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter599352
89.5%
Space Separator35256
 
5.3%
Dash Punctuation35256
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
N105768
17.6%
E105768
17.6%
I70512
11.8%
O70512
11.8%
R70512
11.8%
T70512
11.8%
U35256
 
5.9%
D35256
 
5.9%
C35256
 
5.9%
ValueCountFrequency (%)
35256
100.0%
ValueCountFrequency (%)
-35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin599352
89.5%
Common70512
 
10.5%

Most frequent character per script

ValueCountFrequency (%)
N105768
17.6%
E105768
17.6%
I70512
11.8%
O70512
11.8%
R70512
11.8%
T70512
11.8%
U35256
 
5.9%
D35256
 
5.9%
C35256
 
5.9%
ValueCountFrequency (%)
35256
50.0%
-35256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII669864
100.0%

Most frequent character per block

ValueCountFrequency (%)
N105768
15.8%
E105768
15.8%
I70512
10.5%
O70512
10.5%
R70512
10.5%
T70512
10.5%
U35256
 
5.3%
35256
 
5.3%
D35256
 
5.3%
-35256
 
5.3%

Unnamed: 216
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
UNION DE LA DROITE ET DU CENTRE
35256 

Length

Max length31
Median length31
Mean length31
Min length31

Characters and Unicode

Total characters1092936
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUNION DE LA DROITE ET DU CENTRE
2nd rowUNION DE LA DROITE ET DU CENTRE
3rd rowUNION DE LA DROITE ET DU CENTRE
4th rowUNION DE LA DROITE ET DU CENTRE
5th rowUNION DE LA DROITE ET DU CENTRE
ValueCountFrequency (%)
UNION DE LA DROITE ET DU CENTRE35256
100.0%
2021-02-18T22:27:02.474305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:02.528631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
de35256
14.3%
union35256
14.3%
du35256
14.3%
et35256
14.3%
centre35256
14.3%
la35256
14.3%
droite35256
14.3%

Most occurring characters

ValueCountFrequency (%)
211536
19.4%
E176280
16.1%
N105768
9.7%
D105768
9.7%
T105768
9.7%
U70512
 
6.5%
I70512
 
6.5%
O70512
 
6.5%
R70512
 
6.5%
L35256
 
3.2%
Other values (2)70512
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter881400
80.6%
Space Separator211536
 
19.4%

Most frequent character per category

ValueCountFrequency (%)
E176280
20.0%
N105768
12.0%
D105768
12.0%
T105768
12.0%
U70512
 
8.0%
I70512
 
8.0%
O70512
 
8.0%
R70512
 
8.0%
L35256
 
4.0%
A35256
 
4.0%
ValueCountFrequency (%)
211536
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin881400
80.6%
Common211536
 
19.4%

Most frequent character per script

ValueCountFrequency (%)
E176280
20.0%
N105768
12.0%
D105768
12.0%
T105768
12.0%
U70512
 
8.0%
I70512
 
8.0%
O70512
 
8.0%
R70512
 
8.0%
L35256
 
4.0%
A35256
 
4.0%
ValueCountFrequency (%)
211536
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1092936
100.0%

Most frequent character per block

ValueCountFrequency (%)
211536
19.4%
E176280
16.1%
N105768
9.7%
D105768
9.7%
T105768
9.7%
U70512
 
6.5%
I70512
 
6.5%
O70512
 
6.5%
R70512
 
6.5%
L35256
 
3.2%
Other values (2)70512
 
6.5%

Unnamed: 217
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
BELLAMY François-Xavier
35256 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters810888
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBELLAMY François-Xavier
2nd rowBELLAMY François-Xavier
3rd rowBELLAMY François-Xavier
4th rowBELLAMY François-Xavier
5th rowBELLAMY François-Xavier
ValueCountFrequency (%)
BELLAMY François-Xavier35256
100.0%
2021-02-18T22:27:02.660831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:02.714286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
bellamy35256
50.0%
françois-xavier35256
50.0%

Most occurring characters

ValueCountFrequency (%)
L70512
 
8.7%
r70512
 
8.7%
a70512
 
8.7%
i70512
 
8.7%
B35256
 
4.3%
E35256
 
4.3%
A35256
 
4.3%
M35256
 
4.3%
Y35256
 
4.3%
35256
 
4.3%
Other values (9)317304
39.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter423072
52.2%
Uppercase Letter317304
39.1%
Space Separator35256
 
4.3%
Dash Punctuation35256
 
4.3%

Most frequent character per category

ValueCountFrequency (%)
r70512
16.7%
a70512
16.7%
i70512
16.7%
n35256
8.3%
ç35256
8.3%
o35256
8.3%
s35256
8.3%
v35256
8.3%
e35256
8.3%
ValueCountFrequency (%)
L70512
22.2%
B35256
11.1%
E35256
11.1%
A35256
11.1%
M35256
11.1%
Y35256
11.1%
F35256
11.1%
X35256
11.1%
ValueCountFrequency (%)
35256
100.0%
ValueCountFrequency (%)
-35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin740376
91.3%
Common70512
 
8.7%

Most frequent character per script

ValueCountFrequency (%)
L70512
 
9.5%
r70512
 
9.5%
a70512
 
9.5%
i70512
 
9.5%
B35256
 
4.8%
E35256
 
4.8%
A35256
 
4.8%
M35256
 
4.8%
Y35256
 
4.8%
F35256
 
4.8%
Other values (7)246792
33.3%
ValueCountFrequency (%)
35256
50.0%
-35256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII775632
95.7%
None35256
 
4.3%

Most frequent character per block

ValueCountFrequency (%)
L70512
 
9.1%
r70512
 
9.1%
a70512
 
9.1%
i70512
 
9.1%
B35256
 
4.5%
E35256
 
4.5%
A35256
 
4.5%
M35256
 
4.5%
Y35256
 
4.5%
35256
 
4.5%
Other values (8)282048
36.4%
ValueCountFrequency (%)
ç35256
100.0%

Unnamed: 218
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct841
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.47035966
Minimum0
Maximum75722
Zeros591
Zeros (%)1.7%
Memory size275.6 KiB
2021-02-18T22:27:02.778439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median16
Q338
95-th percentile172
Maximum75722
Range75722
Interquartile range (IQR)30

Descriptive statistics

Standard deviation467.7983351
Coefficient of variation (CV)8.588126425
Kurtosis19538.83576
Mean54.47035966
Median Absolute Deviation (MAD)11
Skewness124.4482709
Sum1920407
Variance218835.2823
MonotocityNot monotonic
2021-02-18T22:27:02.886350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41301
 
3.7%
61282
 
3.6%
81268
 
3.6%
51259
 
3.6%
71238
 
3.5%
91192
 
3.4%
31183
 
3.4%
101176
 
3.3%
111025
 
2.9%
13993
 
2.8%
Other values (831)23339
66.2%
ValueCountFrequency (%)
0591
1.7%
1814
2.3%
2986
2.8%
31183
3.4%
41301
3.7%
ValueCountFrequency (%)
757221
< 0.1%
175831
< 0.1%
157391
< 0.1%
116281
< 0.1%
96141
< 0.1%

Unnamed: 219
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1668
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.985775187
Minimum0
Maximum49.64
Zeros591
Zeros (%)1.7%
Memory size275.6 KiB
2021-02-18T22:27:02.999565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.35
Q13.04
median4.33
Q36.12
95-th percentile10.81
Maximum49.64
Range49.64
Interquartile range (IQR)3.08

Descriptive statistics

Standard deviation3.218825412
Coefficient of variation (CV)0.6456017954
Kurtosis13.02822309
Mean4.985775187
Median Absolute Deviation (MAD)1.48
Skewness2.462891778
Sum175778.49
Variance10.36083703
MonotocityNot monotonic
2021-02-18T22:27:03.112281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0591
 
1.7%
4.55129
 
0.4%
5124
 
0.4%
5.26122
 
0.3%
5.88119
 
0.3%
3.85113
 
0.3%
4.35112
 
0.3%
6.25111
 
0.3%
4111
 
0.3%
4.17109
 
0.3%
Other values (1658)33615
95.3%
ValueCountFrequency (%)
0591
1.7%
0.061
 
< 0.1%
0.081
 
< 0.1%
0.091
 
< 0.1%
0.11
 
< 0.1%
ValueCountFrequency (%)
49.641
< 0.1%
45.951
< 0.1%
45.831
< 0.1%
45.391
< 0.1%
401
< 0.1%

Unnamed: 220
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2438
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.299932777
Minimum0
Maximum75.79
Zeros591
Zeros (%)1.7%
Memory size275.6 KiB
2021-02-18T22:27:03.222495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.76
Q15.88
median8.26
Q311.37
95-th percentile19.35
Maximum75.79
Range75.79
Interquartile range (IQR)5.49

Descriptive statistics

Standard deviation5.58852822
Coefficient of variation (CV)0.6009213565
Kurtosis10.23954194
Mean9.299932777
Median Absolute Deviation (MAD)2.69
Skewness2.178593275
Sum327878.43
Variance31.23164767
MonotocityNot monotonic
2021-02-18T22:27:03.328019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0591
 
1.7%
10209
 
0.6%
8.33202
 
0.6%
11.11179
 
0.5%
9.09175
 
0.5%
7.69171
 
0.5%
12.5164
 
0.5%
7.14163
 
0.5%
6.25153
 
0.4%
6.67142
 
0.4%
Other values (2428)33107
93.9%
ValueCountFrequency (%)
0591
1.7%
0.371
 
< 0.1%
0.551
 
< 0.1%
0.571
 
< 0.1%
0.611
 
< 0.1%
ValueCountFrequency (%)
75.791
< 0.1%
73.331
< 0.1%
72.731
< 0.1%
72.091
< 0.1%
69.71
< 0.1%

Unnamed: 221
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
30
35256 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters70512
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30
2nd row30
3rd row30
4th row30
5th row30
ValueCountFrequency (%)
3035256
100.0%
2021-02-18T22:27:03.505986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:03.558226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
3035256
100.0%

Most occurring characters

ValueCountFrequency (%)
335256
50.0%
035256
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number70512
100.0%

Most frequent character per category

ValueCountFrequency (%)
335256
50.0%
035256
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common70512
100.0%

Most frequent character per script

ValueCountFrequency (%)
335256
50.0%
035256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70512
100.0%

Most frequent character per block

ValueCountFrequency (%)
335256
50.0%
035256
50.0%

Unnamed: 222
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
EUROPE ÉCOLOGIE
35256 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters528840
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEUROPE ÉCOLOGIE
2nd rowEUROPE ÉCOLOGIE
3rd rowEUROPE ÉCOLOGIE
4th rowEUROPE ÉCOLOGIE
5th rowEUROPE ÉCOLOGIE
ValueCountFrequency (%)
EUROPE ÉCOLOGIE35256
100.0%
2021-02-18T22:27:03.687239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:03.740563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
europe35256
50.0%
écologie35256
50.0%

Most occurring characters

ValueCountFrequency (%)
E105768
20.0%
O105768
20.0%
U35256
 
6.7%
R35256
 
6.7%
P35256
 
6.7%
35256
 
6.7%
É35256
 
6.7%
C35256
 
6.7%
L35256
 
6.7%
G35256
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter493584
93.3%
Space Separator35256
 
6.7%

Most frequent character per category

ValueCountFrequency (%)
E105768
21.4%
O105768
21.4%
U35256
 
7.1%
R35256
 
7.1%
P35256
 
7.1%
É35256
 
7.1%
C35256
 
7.1%
L35256
 
7.1%
G35256
 
7.1%
I35256
 
7.1%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin493584
93.3%
Common35256
 
6.7%

Most frequent character per script

ValueCountFrequency (%)
E105768
21.4%
O105768
21.4%
U35256
 
7.1%
R35256
 
7.1%
P35256
 
7.1%
É35256
 
7.1%
C35256
 
7.1%
L35256
 
7.1%
G35256
 
7.1%
I35256
 
7.1%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII493584
93.3%
None35256
 
6.7%

Most frequent character per block

ValueCountFrequency (%)
E105768
21.4%
O105768
21.4%
U35256
 
7.1%
R35256
 
7.1%
P35256
 
7.1%
35256
 
7.1%
C35256
 
7.1%
L35256
 
7.1%
G35256
 
7.1%
I35256
 
7.1%
ValueCountFrequency (%)
É35256
100.0%

Unnamed: 223
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
EUROPE ÉCOLOGIE
35256 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters528840
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEUROPE ÉCOLOGIE
2nd rowEUROPE ÉCOLOGIE
3rd rowEUROPE ÉCOLOGIE
4th rowEUROPE ÉCOLOGIE
5th rowEUROPE ÉCOLOGIE
ValueCountFrequency (%)
EUROPE ÉCOLOGIE35256
100.0%
2021-02-18T22:27:03.870153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:03.922858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
europe35256
50.0%
écologie35256
50.0%

Most occurring characters

ValueCountFrequency (%)
E105768
20.0%
O105768
20.0%
U35256
 
6.7%
R35256
 
6.7%
P35256
 
6.7%
35256
 
6.7%
É35256
 
6.7%
C35256
 
6.7%
L35256
 
6.7%
G35256
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter493584
93.3%
Space Separator35256
 
6.7%

Most frequent character per category

ValueCountFrequency (%)
E105768
21.4%
O105768
21.4%
U35256
 
7.1%
R35256
 
7.1%
P35256
 
7.1%
É35256
 
7.1%
C35256
 
7.1%
L35256
 
7.1%
G35256
 
7.1%
I35256
 
7.1%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin493584
93.3%
Common35256
 
6.7%

Most frequent character per script

ValueCountFrequency (%)
E105768
21.4%
O105768
21.4%
U35256
 
7.1%
R35256
 
7.1%
P35256
 
7.1%
É35256
 
7.1%
C35256
 
7.1%
L35256
 
7.1%
G35256
 
7.1%
I35256
 
7.1%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII493584
93.3%
None35256
 
6.7%

Most frequent character per block

ValueCountFrequency (%)
E105768
21.4%
O105768
21.4%
U35256
 
7.1%
R35256
 
7.1%
P35256
 
7.1%
35256
 
7.1%
C35256
 
7.1%
L35256
 
7.1%
G35256
 
7.1%
I35256
 
7.1%
ValueCountFrequency (%)
É35256
100.0%

Unnamed: 224
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
JADOT Yannick
35256 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters458328
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJADOT Yannick
2nd rowJADOT Yannick
3rd rowJADOT Yannick
4th rowJADOT Yannick
5th rowJADOT Yannick
ValueCountFrequency (%)
JADOT Yannick35256
100.0%
2021-02-18T22:27:04.052660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:04.104538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
yannick35256
50.0%
jadot35256
50.0%

Most occurring characters

ValueCountFrequency (%)
n70512
15.4%
J35256
7.7%
A35256
7.7%
D35256
7.7%
O35256
7.7%
T35256
7.7%
35256
7.7%
Y35256
7.7%
a35256
7.7%
i35256
7.7%
Other values (2)70512
15.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter211536
46.2%
Lowercase Letter211536
46.2%
Space Separator35256
 
7.7%

Most frequent character per category

ValueCountFrequency (%)
J35256
16.7%
A35256
16.7%
D35256
16.7%
O35256
16.7%
T35256
16.7%
Y35256
16.7%
ValueCountFrequency (%)
n70512
33.3%
a35256
16.7%
i35256
16.7%
c35256
16.7%
k35256
16.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin423072
92.3%
Common35256
 
7.7%

Most frequent character per script

ValueCountFrequency (%)
n70512
16.7%
J35256
8.3%
A35256
8.3%
D35256
8.3%
O35256
8.3%
T35256
8.3%
Y35256
8.3%
a35256
8.3%
i35256
8.3%
c35256
8.3%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII458328
100.0%

Most frequent character per block

ValueCountFrequency (%)
n70512
15.4%
J35256
7.7%
A35256
7.7%
D35256
7.7%
O35256
7.7%
T35256
7.7%
35256
7.7%
Y35256
7.7%
a35256
7.7%
i35256
7.7%
Other values (2)70512
15.4%

Unnamed: 225
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct1128
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.65256978
Minimum0
Maximum148377
Zeros545
Zeros (%)1.5%
Memory size275.6 KiB
2021-02-18T22:27:04.166257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median20
Q353.25
95-th percentile284
Maximum148377
Range148377
Interquartile range (IQR)45.25

Descriptive statistics

Standard deviation905.0238439
Coefficient of variation (CV)10.44428164
Kurtosis20564.76842
Mean86.65256978
Median Absolute Deviation (MAD)15
Skewness129.1496994
Sum3055023
Variance819068.1581
MonotocityNot monotonic
2021-02-18T22:27:04.272785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51173
 
3.3%
61147
 
3.3%
41123
 
3.2%
71093
 
3.1%
31063
 
3.0%
81007
 
2.9%
91005
 
2.9%
2992
 
2.8%
11906
 
2.6%
10887
 
2.5%
Other values (1118)24860
70.5%
ValueCountFrequency (%)
0545
1.5%
1794
2.3%
2992
2.8%
31063
3.0%
41123
3.2%
ValueCountFrequency (%)
1483771
< 0.1%
318651
< 0.1%
291201
< 0.1%
265391
< 0.1%
238381
< 0.1%

Unnamed: 226
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1741
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.086665816
Minimum0
Maximum67.57
Zeros545
Zeros (%)1.5%
Memory size275.6 KiB
2021-02-18T22:27:04.387336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.74
Q13.97
median5.66
Q37.69
95-th percentile11.59
Maximum67.57
Range67.57
Interquartile range (IQR)3.72

Descriptive statistics

Standard deviation3.301905657
Coefficient of variation (CV)0.5424818376
Kurtosis15.22273089
Mean6.086665816
Median Absolute Deviation (MAD)1.83
Skewness2.052178555
Sum214591.49
Variance10.90258097
MonotocityNot monotonic
2021-02-18T22:27:04.495678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0545
 
1.5%
7.14125
 
0.4%
5.88122
 
0.3%
6.67121
 
0.3%
4.55118
 
0.3%
5.56117
 
0.3%
5113
 
0.3%
8.33111
 
0.3%
4.76110
 
0.3%
5.26110
 
0.3%
Other values (1731)33664
95.5%
ValueCountFrequency (%)
0545
1.5%
0.11
 
< 0.1%
0.182
 
< 0.1%
0.241
 
< 0.1%
0.251
 
< 0.1%
ValueCountFrequency (%)
67.571
< 0.1%
61.111
< 0.1%
47.221
< 0.1%
46.151
< 0.1%
45.511
< 0.1%

Unnamed: 227
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2609
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.42112832
Minimum0
Maximum100
Zeros545
Zeros (%)1.5%
Memory size275.6 KiB
2021-02-18T22:27:04.612339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.45
Q17.73
median10.86
Q314.39
95-th percentile20.74
Maximum100
Range100
Interquartile range (IQR)6.66

Descriptive statistics

Standard deviation5.823475612
Coefficient of variation (CV)0.5098861907
Kurtosis13.54422314
Mean11.42112832
Median Absolute Deviation (MAD)3.31
Skewness1.893115956
Sum402663.3
Variance33.9128682
MonotocityNot monotonic
2021-02-18T22:27:04.722078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0545
 
1.5%
11.11232
 
0.7%
9.09199
 
0.6%
10196
 
0.6%
14.29193
 
0.5%
12.5182
 
0.5%
8.33176
 
0.5%
7.69153
 
0.4%
7.14143
 
0.4%
6.67132
 
0.4%
Other values (2599)33105
93.9%
ValueCountFrequency (%)
0545
1.5%
0.31
 
< 0.1%
0.721
 
< 0.1%
0.791
 
< 0.1%
0.82
 
< 0.1%
ValueCountFrequency (%)
1002
< 0.1%
95.651
< 0.1%
79.411
< 0.1%
78.131
< 0.1%
751
< 0.1%

Unnamed: 228
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
31
35256 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters70512
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row31
2nd row31
3rd row31
4th row31
5th row31
ValueCountFrequency (%)
3135256
100.0%
2021-02-18T22:27:04.893241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:04.945220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
3135256
100.0%

Most occurring characters

ValueCountFrequency (%)
335256
50.0%
135256
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number70512
100.0%

Most frequent character per category

ValueCountFrequency (%)
335256
50.0%
135256
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common70512
100.0%

Most frequent character per script

ValueCountFrequency (%)
335256
50.0%
135256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70512
100.0%

Most frequent character per block

ValueCountFrequency (%)
335256
50.0%
135256
50.0%

Unnamed: 229
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
PARTI ANIMALISTE
35256 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters564096
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPARTI ANIMALISTE
2nd rowPARTI ANIMALISTE
3rd rowPARTI ANIMALISTE
4th rowPARTI ANIMALISTE
5th rowPARTI ANIMALISTE
ValueCountFrequency (%)
PARTI ANIMALISTE35256
100.0%
2021-02-18T22:27:05.074267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:05.127194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
parti35256
50.0%
animaliste35256
50.0%

Most occurring characters

ValueCountFrequency (%)
A105768
18.8%
I105768
18.8%
T70512
12.5%
P35256
 
6.2%
R35256
 
6.2%
35256
 
6.2%
N35256
 
6.2%
M35256
 
6.2%
L35256
 
6.2%
S35256
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter528840
93.8%
Space Separator35256
 
6.2%

Most frequent character per category

ValueCountFrequency (%)
A105768
20.0%
I105768
20.0%
T70512
13.3%
P35256
 
6.7%
R35256
 
6.7%
N35256
 
6.7%
M35256
 
6.7%
L35256
 
6.7%
S35256
 
6.7%
E35256
 
6.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin528840
93.8%
Common35256
 
6.2%

Most frequent character per script

ValueCountFrequency (%)
A105768
20.0%
I105768
20.0%
T70512
13.3%
P35256
 
6.7%
R35256
 
6.7%
N35256
 
6.7%
M35256
 
6.7%
L35256
 
6.7%
S35256
 
6.7%
E35256
 
6.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII564096
100.0%

Most frequent character per block

ValueCountFrequency (%)
A105768
18.8%
I105768
18.8%
T70512
12.5%
P35256
 
6.2%
R35256
 
6.2%
35256
 
6.2%
N35256
 
6.2%
M35256
 
6.2%
L35256
 
6.2%
S35256
 
6.2%

Unnamed: 230
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
PARTI ANIMALISTE
35256 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters564096
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPARTI ANIMALISTE
2nd rowPARTI ANIMALISTE
3rd rowPARTI ANIMALISTE
4th rowPARTI ANIMALISTE
5th rowPARTI ANIMALISTE
ValueCountFrequency (%)
PARTI ANIMALISTE35256
100.0%
2021-02-18T22:27:05.257236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:05.310111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
parti35256
50.0%
animaliste35256
50.0%

Most occurring characters

ValueCountFrequency (%)
A105768
18.8%
I105768
18.8%
T70512
12.5%
P35256
 
6.2%
R35256
 
6.2%
35256
 
6.2%
N35256
 
6.2%
M35256
 
6.2%
L35256
 
6.2%
S35256
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter528840
93.8%
Space Separator35256
 
6.2%

Most frequent character per category

ValueCountFrequency (%)
A105768
20.0%
I105768
20.0%
T70512
13.3%
P35256
 
6.7%
R35256
 
6.7%
N35256
 
6.7%
M35256
 
6.7%
L35256
 
6.7%
S35256
 
6.7%
E35256
 
6.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin528840
93.8%
Common35256
 
6.2%

Most frequent character per script

ValueCountFrequency (%)
A105768
20.0%
I105768
20.0%
T70512
13.3%
P35256
 
6.7%
R35256
 
6.7%
N35256
 
6.7%
M35256
 
6.7%
L35256
 
6.7%
S35256
 
6.7%
E35256
 
6.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII564096
100.0%

Most frequent character per block

ValueCountFrequency (%)
A105768
18.8%
I105768
18.8%
T70512
12.5%
P35256
 
6.2%
R35256
 
6.2%
35256
 
6.2%
N35256
 
6.2%
M35256
 
6.2%
L35256
 
6.2%
S35256
 
6.2%

Unnamed: 231
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
THOUY Hélène
35256 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters423072
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTHOUY Hélène
2nd rowTHOUY Hélène
3rd rowTHOUY Hélène
4th rowTHOUY Hélène
5th rowTHOUY Hélène
ValueCountFrequency (%)
THOUY Hélène35256
100.0%
2021-02-18T22:27:05.440383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:05.493356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
thouy35256
50.0%
hélène35256
50.0%

Most occurring characters

ValueCountFrequency (%)
H70512
16.7%
T35256
8.3%
O35256
8.3%
U35256
8.3%
Y35256
8.3%
35256
8.3%
é35256
8.3%
l35256
8.3%
è35256
8.3%
n35256
8.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter211536
50.0%
Lowercase Letter176280
41.7%
Space Separator35256
 
8.3%

Most frequent character per category

ValueCountFrequency (%)
H70512
33.3%
T35256
16.7%
O35256
16.7%
U35256
16.7%
Y35256
16.7%
ValueCountFrequency (%)
é35256
20.0%
l35256
20.0%
è35256
20.0%
n35256
20.0%
e35256
20.0%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin387816
91.7%
Common35256
 
8.3%

Most frequent character per script

ValueCountFrequency (%)
H70512
18.2%
T35256
9.1%
O35256
9.1%
U35256
9.1%
Y35256
9.1%
é35256
9.1%
l35256
9.1%
è35256
9.1%
n35256
9.1%
e35256
9.1%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII352560
83.3%
None70512
 
16.7%

Most frequent character per block

ValueCountFrequency (%)
H70512
20.0%
T35256
10.0%
O35256
10.0%
U35256
10.0%
Y35256
10.0%
35256
10.0%
l35256
10.0%
n35256
10.0%
e35256
10.0%
ValueCountFrequency (%)
é35256
50.0%
è35256
50.0%

Unnamed: 232
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct395
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.90044248
Minimum0
Maximum9503
Zeros4723
Zeros (%)13.4%
Memory size275.6 KiB
2021-02-18T22:27:05.556344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q310
95-th percentile49
Maximum9503
Range9503
Interquartile range (IQR)9

Descriptive statistics

Standard deviation70.98852523
Coefficient of variation (CV)5.106925578
Kurtosis9292.941129
Mean13.90044248
Median Absolute Deviation (MAD)3
Skewness75.737328
Sum490074
Variance5039.370715
MonotocityNot monotonic
2021-02-18T22:27:05.663568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04723
13.4%
14351
12.3%
23693
 
10.5%
33112
 
8.8%
42536
 
7.2%
52070
 
5.9%
61679
 
4.8%
71391
 
3.9%
81168
 
3.3%
9932
 
2.6%
Other values (385)9601
27.2%
ValueCountFrequency (%)
04723
13.4%
14351
12.3%
23693
10.5%
33112
8.8%
42536
7.2%
ValueCountFrequency (%)
95031
< 0.1%
31411
< 0.1%
27361
< 0.1%
22251
< 0.1%
18871
< 0.1%

Unnamed: 233
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct604
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.228607897
Minimum0
Maximum20
Zeros4724
Zeros (%)13.4%
Memory size275.6 KiB
2021-02-18T22:27:05.774097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.61
median1.08
Q31.63
95-th percentile3
Maximum20
Range20
Interquartile range (IQR)1.02

Descriptive statistics

Standard deviation1.025226081
Coefficient of variation (CV)0.8344615755
Kurtosis15.08908227
Mean1.228607897
Median Absolute Deviation (MAD)0.51
Skewness2.368060701
Sum43315.8
Variance1.051088517
MonotocityNot monotonic
2021-02-18T22:27:05.887073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04724
 
13.4%
0.93258
 
0.7%
0.9242
 
0.7%
0.85240
 
0.7%
1.22223
 
0.6%
1.2221
 
0.6%
0.99220
 
0.6%
1.02218
 
0.6%
1.05217
 
0.6%
1.14216
 
0.6%
Other values (594)28477
80.8%
ValueCountFrequency (%)
04724
13.4%
0.0116
 
< 0.1%
0.028
 
< 0.1%
0.038
 
< 0.1%
0.049
 
< 0.1%
ValueCountFrequency (%)
201
< 0.1%
15.911
< 0.1%
14.711
< 0.1%
12.81
< 0.1%
12.771
< 0.1%

Unnamed: 234
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct882
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.293859201
Minimum0
Maximum33.33
Zeros4723
Zeros (%)13.4%
Memory size275.6 KiB
2021-02-18T22:27:06.005257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.16
median2.07
Q33.08
95-th percentile5.45
Maximum33.33
Range33.33
Interquartile range (IQR)1.92

Descriptive statistics

Standard deviation1.822345679
Coefficient of variation (CV)0.7944453076
Kurtosis11.57452188
Mean2.293859201
Median Absolute Deviation (MAD)0.96
Skewness2.015707213
Sum80872.3
Variance3.320943773
MonotocityNot monotonic
2021-02-18T22:27:07.360685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04723
 
13.4%
2.33177
 
0.5%
2.13167
 
0.5%
2.78160
 
0.5%
3.23159
 
0.5%
2.56158
 
0.4%
2.86157
 
0.4%
1.96157
 
0.4%
2.04156
 
0.4%
2.5156
 
0.4%
Other values (872)29086
82.5%
ValueCountFrequency (%)
04723
13.4%
0.031
 
< 0.1%
0.041
 
< 0.1%
0.051
 
< 0.1%
0.062
 
< 0.1%
ValueCountFrequency (%)
33.331
< 0.1%
26.921
< 0.1%
23.811
< 0.1%
23.081
< 0.1%
22.221
< 0.1%

Unnamed: 235
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
32
35256 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters70512
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row32
2nd row32
3rd row32
4th row32
5th row32
ValueCountFrequency (%)
3235256
100.0%
2021-02-18T22:27:07.569743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:07.621258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
3235256
100.0%

Most occurring characters

ValueCountFrequency (%)
335256
50.0%
235256
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number70512
100.0%

Most frequent character per category

ValueCountFrequency (%)
335256
50.0%
235256
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common70512
100.0%

Most frequent character per script

ValueCountFrequency (%)
335256
50.0%
235256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70512
100.0%

Most frequent character per block

ValueCountFrequency (%)
335256
50.0%
235256
50.0%

Unnamed: 236
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
LES OUBLIES DE L'EUROPE
35256 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters810888
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLES OUBLIES DE L'EUROPE
2nd rowLES OUBLIES DE L'EUROPE
3rd rowLES OUBLIES DE L'EUROPE
4th rowLES OUBLIES DE L'EUROPE
5th rowLES OUBLIES DE L'EUROPE
ValueCountFrequency (%)
LES OUBLIES DE L'EUROPE35256
100.0%
2021-02-18T22:27:07.753734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:07.803362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
de35256
25.0%
l'europe35256
25.0%
oublies35256
25.0%
les35256
25.0%

Most occurring characters

ValueCountFrequency (%)
E176280
21.7%
L105768
13.0%
105768
13.0%
S70512
 
8.7%
O70512
 
8.7%
U70512
 
8.7%
B35256
 
4.3%
I35256
 
4.3%
D35256
 
4.3%
'35256
 
4.3%
Other values (2)70512
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter669864
82.6%
Space Separator105768
 
13.0%
Other Punctuation35256
 
4.3%

Most frequent character per category

ValueCountFrequency (%)
E176280
26.3%
L105768
15.8%
S70512
 
10.5%
O70512
 
10.5%
U70512
 
10.5%
B35256
 
5.3%
I35256
 
5.3%
D35256
 
5.3%
R35256
 
5.3%
P35256
 
5.3%
ValueCountFrequency (%)
105768
100.0%
ValueCountFrequency (%)
'35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin669864
82.6%
Common141024
 
17.4%

Most frequent character per script

ValueCountFrequency (%)
E176280
26.3%
L105768
15.8%
S70512
 
10.5%
O70512
 
10.5%
U70512
 
10.5%
B35256
 
5.3%
I35256
 
5.3%
D35256
 
5.3%
R35256
 
5.3%
P35256
 
5.3%
ValueCountFrequency (%)
105768
75.0%
'35256
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII810888
100.0%

Most frequent character per block

ValueCountFrequency (%)
E176280
21.7%
L105768
13.0%
105768
13.0%
S70512
 
8.7%
O70512
 
8.7%
U70512
 
8.7%
B35256
 
4.3%
I35256
 
4.3%
D35256
 
4.3%
'35256
 
4.3%
Other values (2)70512
 
8.7%

Unnamed: 237
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
LES OUBLIÉS DE L'EUROPE - ARTISANS, COMMERÇANTS, PROFESSIONS LIBÉRALES ET INDÉPENDANTS - ACPLI -
35256 

Length

Max length96
Median length96
Mean length96
Min length96

Characters and Unicode

Total characters3384576
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLES OUBLIÉS DE L'EUROPE - ARTISANS, COMMERÇANTS, PROFESSIONS LIBÉRALES ET INDÉPENDANTS - ACPLI -
2nd rowLES OUBLIÉS DE L'EUROPE - ARTISANS, COMMERÇANTS, PROFESSIONS LIBÉRALES ET INDÉPENDANTS - ACPLI -
3rd rowLES OUBLIÉS DE L'EUROPE - ARTISANS, COMMERÇANTS, PROFESSIONS LIBÉRALES ET INDÉPENDANTS - ACPLI -
4th rowLES OUBLIÉS DE L'EUROPE - ARTISANS, COMMERÇANTS, PROFESSIONS LIBÉRALES ET INDÉPENDANTS - ACPLI -
5th rowLES OUBLIÉS DE L'EUROPE - ARTISANS, COMMERÇANTS, PROFESSIONS LIBÉRALES ET INDÉPENDANTS - ACPLI -
ValueCountFrequency (%)
LES OUBLIÉS DE L'EUROPE - ARTISANS, COMMERÇANTS, PROFESSIONS LIBÉRALES ET INDÉPENDANTS - ACPLI -35256
100.0%
2021-02-18T22:27:07.925129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:07.975103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
105768
21.4%
oubliés35256
 
7.1%
de35256
 
7.1%
professions35256
 
7.1%
et35256
 
7.1%
acpli35256
 
7.1%
artisans35256
 
7.1%
indépendants35256
 
7.1%
l'europe35256
 
7.1%
les35256
 
7.1%
Other values (2)70512
14.3%

Most occurring characters

ValueCountFrequency (%)
458328
13.5%
S352560
 
10.4%
E317304
 
9.4%
L211536
 
6.2%
I211536
 
6.2%
A211536
 
6.2%
N211536
 
6.2%
O176280
 
5.2%
R176280
 
5.2%
P141024
 
4.2%
Other values (12)916656
27.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2714712
80.2%
Space Separator458328
 
13.5%
Other Punctuation105768
 
3.1%
Dash Punctuation105768
 
3.1%

Most frequent character per category

ValueCountFrequency (%)
S352560
13.0%
E317304
11.7%
L211536
 
7.8%
I211536
 
7.8%
A211536
 
7.8%
N211536
 
7.8%
O176280
 
6.5%
R176280
 
6.5%
P141024
 
5.2%
T141024
 
5.2%
Other values (8)564096
20.8%
ValueCountFrequency (%)
,70512
66.7%
'35256
33.3%
ValueCountFrequency (%)
458328
100.0%
ValueCountFrequency (%)
-105768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2714712
80.2%
Common669864
 
19.8%

Most frequent character per script

ValueCountFrequency (%)
S352560
13.0%
E317304
11.7%
L211536
 
7.8%
I211536
 
7.8%
A211536
 
7.8%
N211536
 
7.8%
O176280
 
6.5%
R176280
 
6.5%
P141024
 
5.2%
T141024
 
5.2%
Other values (8)564096
20.8%
ValueCountFrequency (%)
458328
68.4%
-105768
 
15.8%
,70512
 
10.5%
'35256
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3243552
95.8%
None141024
 
4.2%

Most frequent character per block

ValueCountFrequency (%)
458328
14.1%
S352560
10.9%
E317304
9.8%
L211536
 
6.5%
I211536
 
6.5%
A211536
 
6.5%
N211536
 
6.5%
O176280
 
5.4%
R176280
 
5.4%
P141024
 
4.3%
Other values (10)775632
23.9%
ValueCountFrequency (%)
É105768
75.0%
Ç35256
 
25.0%

Unnamed: 238
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
BIDOU Olivier
35256 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters458328
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBIDOU Olivier
2nd rowBIDOU Olivier
3rd rowBIDOU Olivier
4th rowBIDOU Olivier
5th rowBIDOU Olivier
ValueCountFrequency (%)
BIDOU Olivier35256
100.0%
2021-02-18T22:27:08.100899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:08.151155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
bidou35256
50.0%
olivier35256
50.0%

Most occurring characters

ValueCountFrequency (%)
O70512
15.4%
i70512
15.4%
B35256
7.7%
I35256
7.7%
D35256
7.7%
U35256
7.7%
35256
7.7%
l35256
7.7%
v35256
7.7%
e35256
7.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter211536
46.2%
Lowercase Letter211536
46.2%
Space Separator35256
 
7.7%

Most frequent character per category

ValueCountFrequency (%)
O70512
33.3%
B35256
16.7%
I35256
16.7%
D35256
16.7%
U35256
16.7%
ValueCountFrequency (%)
i70512
33.3%
l35256
16.7%
v35256
16.7%
e35256
16.7%
r35256
16.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin423072
92.3%
Common35256
 
7.7%

Most frequent character per script

ValueCountFrequency (%)
O70512
16.7%
i70512
16.7%
B35256
8.3%
I35256
8.3%
D35256
8.3%
U35256
8.3%
l35256
8.3%
v35256
8.3%
e35256
8.3%
r35256
8.3%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII458328
100.0%

Most frequent character per block

ValueCountFrequency (%)
O70512
15.4%
i70512
15.4%
B35256
7.7%
I35256
7.7%
D35256
7.7%
U35256
7.7%
35256
7.7%
l35256
7.7%
v35256
7.7%
e35256
7.7%

Unnamed: 239
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct77
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.453369639
Minimum0
Maximum588
Zeros19198
Zeros (%)54.5%
Memory size275.6 KiB
2021-02-18T22:27:08.212506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile6
Maximum588
Range588
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5.416137345
Coefficient of variation (CV)3.726606914
Kurtosis4348.486872
Mean1.453369639
Median Absolute Deviation (MAD)0
Skewness47.7505561
Sum51240
Variance29.33454374
MonotocityNot monotonic
2021-02-18T22:27:08.316133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019198
54.5%
17351
 
20.9%
23410
 
9.7%
31713
 
4.9%
41044
 
3.0%
5637
 
1.8%
6397
 
1.1%
7299
 
0.8%
8241
 
0.7%
9161
 
0.5%
Other values (67)805
 
2.3%
ValueCountFrequency (%)
019198
54.5%
17351
 
20.9%
23410
 
9.7%
31713
 
4.9%
41044
 
3.0%
ValueCountFrequency (%)
5881
< 0.1%
3181
< 0.1%
1791
< 0.1%
1601
< 0.1%
1301
< 0.1%

Unnamed: 240
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct253
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1616510665
Minimum0
Maximum6.9
Zeros19205
Zeros (%)54.5%
Memory size275.6 KiB
2021-02-18T22:27:08.422270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.2
95-th percentile0.72
Maximum6.9
Range6.9
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.3264735397
Coefficient of variation (CV)2.019618842
Kurtosis42.13398407
Mean0.1616510665
Median Absolute Deviation (MAD)0
Skewness4.865852689
Sum5699.17
Variance0.1065849721
MonotocityNot monotonic
2021-02-18T22:27:08.520638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019205
54.5%
0.11502
 
1.4%
0.1500
 
1.4%
0.09488
 
1.4%
0.14475
 
1.3%
0.12468
 
1.3%
0.15464
 
1.3%
0.08445
 
1.3%
0.13440
 
1.2%
0.18426
 
1.2%
Other values (243)11843
33.6%
ValueCountFrequency (%)
019205
54.5%
0.0135
 
0.1%
0.0264
 
0.2%
0.03129
 
0.4%
0.04211
 
0.6%
ValueCountFrequency (%)
6.91
< 0.1%
6.121
< 0.1%
5.561
< 0.1%
5.431
< 0.1%
5.412
< 0.1%

Unnamed: 241
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct342
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3015620036
Minimum0
Maximum10.53
Zeros19198
Zeros (%)54.5%
Memory size275.6 KiB
2021-02-18T22:27:08.636340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.4
95-th percentile1.33
Maximum10.53
Range10.53
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.5798551817
Coefficient of variation (CV)1.922839001
Kurtosis31.45464567
Mean0.3015620036
Median Absolute Deviation (MAD)0
Skewness4.280307712
Sum10631.87
Variance0.3362320318
MonotocityNot monotonic
2021-02-18T22:27:08.777694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019198
54.5%
0.21285
 
0.8%
0.17256
 
0.7%
0.25253
 
0.7%
0.23252
 
0.7%
0.19252
 
0.7%
0.22251
 
0.7%
0.26250
 
0.7%
0.28246
 
0.7%
0.29245
 
0.7%
Other values (332)13768
39.1%
ValueCountFrequency (%)
019198
54.5%
0.014
 
< 0.1%
0.0214
 
< 0.1%
0.0310
 
< 0.1%
0.0422
 
0.1%
ValueCountFrequency (%)
10.531
< 0.1%
101
< 0.1%
9.521
< 0.1%
9.261
< 0.1%
8.332
< 0.1%

Unnamed: 242
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
33
35256 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters70512
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row33
2nd row33
3rd row33
4th row33
5th row33
ValueCountFrequency (%)
3335256
100.0%
2021-02-18T22:27:08.944757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:08.993420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
3335256
100.0%

Most occurring characters

ValueCountFrequency (%)
370512
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number70512
100.0%

Most frequent character per category

ValueCountFrequency (%)
370512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common70512
100.0%

Most frequent character per script

ValueCountFrequency (%)
370512
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70512
100.0%

Most frequent character per block

ValueCountFrequency (%)
370512
100.0%

Unnamed: 243
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
UDLEF
35256 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters176280
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUDLEF
2nd rowUDLEF
3rd rowUDLEF
4th rowUDLEF
5th rowUDLEF
ValueCountFrequency (%)
UDLEF35256
100.0%
2021-02-18T22:27:09.112954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:09.161613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
udlef35256
100.0%

Most occurring characters

ValueCountFrequency (%)
U35256
20.0%
D35256
20.0%
L35256
20.0%
E35256
20.0%
F35256
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter176280
100.0%

Most frequent character per category

ValueCountFrequency (%)
U35256
20.0%
D35256
20.0%
L35256
20.0%
E35256
20.0%
F35256
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin176280
100.0%

Most frequent character per script

ValueCountFrequency (%)
U35256
20.0%
D35256
20.0%
L35256
20.0%
E35256
20.0%
F35256
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII176280
100.0%

Most frequent character per block

ValueCountFrequency (%)
U35256
20.0%
D35256
20.0%
L35256
20.0%
E35256
20.0%
F35256
20.0%

Unnamed: 244
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
UDLEF (UNION DÉMOCRATIQUE POUR LA LIBERTÉ ÉGALITÉ FRATERNITÉ)
35256 

Length

Max length61
Median length61
Mean length61
Min length61

Characters and Unicode

Total characters2150616
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUDLEF (UNION DÉMOCRATIQUE POUR LA LIBERTÉ ÉGALITÉ FRATERNITÉ)
2nd rowUDLEF (UNION DÉMOCRATIQUE POUR LA LIBERTÉ ÉGALITÉ FRATERNITÉ)
3rd rowUDLEF (UNION DÉMOCRATIQUE POUR LA LIBERTÉ ÉGALITÉ FRATERNITÉ)
4th rowUDLEF (UNION DÉMOCRATIQUE POUR LA LIBERTÉ ÉGALITÉ FRATERNITÉ)
5th rowUDLEF (UNION DÉMOCRATIQUE POUR LA LIBERTÉ ÉGALITÉ FRATERNITÉ)
ValueCountFrequency (%)
UDLEF (UNION DÉMOCRATIQUE POUR LA LIBERTÉ ÉGALITÉ FRATERNITÉ)35256
100.0%
2021-02-18T22:27:09.281546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:09.331185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
liberté35256
12.5%
pour35256
12.5%
union35256
12.5%
démocratique35256
12.5%
égalité35256
12.5%
udlef35256
12.5%
la35256
12.5%
fraternité35256
12.5%

Most occurring characters

ValueCountFrequency (%)
246792
11.5%
I176280
 
8.2%
É176280
 
8.2%
R176280
 
8.2%
T176280
 
8.2%
U141024
 
6.6%
L141024
 
6.6%
E141024
 
6.6%
A141024
 
6.6%
N105768
 
4.9%
Other values (11)528840
24.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1833312
85.2%
Space Separator246792
 
11.5%
Open Punctuation35256
 
1.6%
Close Punctuation35256
 
1.6%

Most frequent character per category

ValueCountFrequency (%)
I176280
9.6%
É176280
9.6%
R176280
9.6%
T176280
9.6%
U141024
 
7.7%
L141024
 
7.7%
E141024
 
7.7%
A141024
 
7.7%
N105768
 
5.8%
O105768
 
5.8%
Other values (8)352560
19.2%
ValueCountFrequency (%)
246792
100.0%
ValueCountFrequency (%)
(35256
100.0%
ValueCountFrequency (%)
)35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1833312
85.2%
Common317304
 
14.8%

Most frequent character per script

ValueCountFrequency (%)
I176280
9.6%
É176280
9.6%
R176280
9.6%
T176280
9.6%
U141024
 
7.7%
L141024
 
7.7%
E141024
 
7.7%
A141024
 
7.7%
N105768
 
5.8%
O105768
 
5.8%
Other values (8)352560
19.2%
ValueCountFrequency (%)
246792
77.8%
(35256
 
11.1%
)35256
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1974336
91.8%
None176280
 
8.2%

Most frequent character per block

ValueCountFrequency (%)
246792
12.5%
I176280
8.9%
R176280
8.9%
T176280
8.9%
U141024
 
7.1%
L141024
 
7.1%
E141024
 
7.1%
A141024
 
7.1%
N105768
 
5.4%
O105768
 
5.4%
Other values (10)423072
21.4%
ValueCountFrequency (%)
É176280
100.0%

Unnamed: 245
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
PERSON Christian Luc
35256 

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters705120
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPERSON Christian Luc
2nd rowPERSON Christian Luc
3rd rowPERSON Christian Luc
4th rowPERSON Christian Luc
5th rowPERSON Christian Luc
ValueCountFrequency (%)
PERSON Christian Luc35256
100.0%
2021-02-18T22:27:09.454589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:09.504257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
person35256
33.3%
luc35256
33.3%
christian35256
33.3%

Most occurring characters

ValueCountFrequency (%)
70512
 
10.0%
i70512
 
10.0%
P35256
 
5.0%
E35256
 
5.0%
R35256
 
5.0%
S35256
 
5.0%
O35256
 
5.0%
N35256
 
5.0%
C35256
 
5.0%
h35256
 
5.0%
Other values (8)282048
40.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter352560
50.0%
Uppercase Letter282048
40.0%
Space Separator70512
 
10.0%

Most frequent character per category

ValueCountFrequency (%)
i70512
20.0%
h35256
10.0%
r35256
10.0%
s35256
10.0%
t35256
10.0%
a35256
10.0%
n35256
10.0%
u35256
10.0%
c35256
10.0%
ValueCountFrequency (%)
P35256
12.5%
E35256
12.5%
R35256
12.5%
S35256
12.5%
O35256
12.5%
N35256
12.5%
C35256
12.5%
L35256
12.5%
ValueCountFrequency (%)
70512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin634608
90.0%
Common70512
 
10.0%

Most frequent character per script

ValueCountFrequency (%)
i70512
 
11.1%
P35256
 
5.6%
E35256
 
5.6%
R35256
 
5.6%
S35256
 
5.6%
O35256
 
5.6%
N35256
 
5.6%
C35256
 
5.6%
h35256
 
5.6%
r35256
 
5.6%
Other values (7)246792
38.9%
ValueCountFrequency (%)
70512
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII705120
100.0%

Most frequent character per block

ValueCountFrequency (%)
70512
 
10.0%
i70512
 
10.0%
P35256
 
5.0%
E35256
 
5.0%
R35256
 
5.0%
S35256
 
5.0%
O35256
 
5.0%
N35256
 
5.0%
C35256
 
5.0%
h35256
 
5.0%
Other values (8)282048
40.0%

Unnamed: 246
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.139323803
Minimum0
Maximum326
Zeros34291
Zeros (%)97.3%
Memory size275.6 KiB
2021-02-18T22:27:09.559979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum326
Range326
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.255321267
Coefficient of variation (CV)16.18762349
Kurtosis12504.82418
Mean0.139323803
Median Absolute Deviation (MAD)0
Skewness91.49805916
Sum4912
Variance5.086474017
MonotocityNot monotonic
2021-02-18T22:27:09.652501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
034291
97.3%
1433
 
1.2%
2146
 
0.4%
387
 
0.2%
436
 
0.1%
533
 
0.1%
633
 
0.1%
826
 
0.1%
718
 
0.1%
1018
 
0.1%
Other values (32)135
 
0.4%
ValueCountFrequency (%)
034291
97.3%
1433
 
1.2%
2146
 
0.4%
387
 
0.2%
436
 
0.1%
ValueCountFrequency (%)
3261
< 0.1%
971
< 0.1%
501
< 0.1%
411
< 0.1%
401
< 0.1%

Unnamed: 247
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct74
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003300714772
Minimum0
Maximum2.35
Zeros34333
Zeros (%)97.4%
Memory size275.6 KiB
2021-02-18T22:27:09.752276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2.35
Range2.35
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.03686686801
Coefficient of variation (CV)11.16935893
Kurtosis1231.740337
Mean0.003300714772
Median Absolute Deviation (MAD)0
Skewness28.23463912
Sum116.37
Variance0.001359165957
MonotocityNot monotonic
2021-02-18T22:27:09.853866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034333
97.4%
0.0481
 
0.2%
0.0576
 
0.2%
0.0375
 
0.2%
0.0674
 
0.2%
0.0265
 
0.2%
0.0161
 
0.2%
0.0756
 
0.2%
0.0854
 
0.2%
0.0949
 
0.1%
Other values (64)332
 
0.9%
ValueCountFrequency (%)
034333
97.4%
0.0161
 
0.2%
0.0265
 
0.2%
0.0375
 
0.2%
0.0481
 
0.2%
ValueCountFrequency (%)
2.351
< 0.1%
2.111
< 0.1%
1.681
< 0.1%
1.591
< 0.1%
1.541
< 0.1%

Unnamed: 248
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct110
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00649478103
Minimum0
Maximum4.69
Zeros34309
Zeros (%)97.3%
Memory size275.6 KiB
2021-02-18T22:27:09.953012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4.69
Range4.69
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.06861653831
Coefficient of variation (CV)10.56487324
Kurtosis1229.093441
Mean0.00649478103
Median Absolute Deviation (MAD)0
Skewness27.32641428
Sum228.98
Variance0.00470822933
MonotocityNot monotonic
2021-02-18T22:27:10.053078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034309
97.3%
0.141
 
0.1%
0.0941
 
0.1%
0.0639
 
0.1%
0.0839
 
0.1%
0.0138
 
0.1%
0.0337
 
0.1%
0.0537
 
0.1%
0.1135
 
0.1%
0.1332
 
0.1%
Other values (100)608
 
1.7%
ValueCountFrequency (%)
034309
97.3%
0.0138
 
0.1%
0.0222
 
0.1%
0.0337
 
0.1%
0.0432
 
0.1%
ValueCountFrequency (%)
4.691
< 0.1%
3.71
< 0.1%
2.991
< 0.1%
2.781
< 0.1%
2.561
< 0.1%

Unnamed: 249
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
34
35256 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters70512
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row34
2nd row34
3rd row34
4th row34
5th row34
ValueCountFrequency (%)
3435256
100.0%
2021-02-18T22:27:10.215055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:10.263699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
3435256
100.0%

Most occurring characters

ValueCountFrequency (%)
335256
50.0%
435256
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number70512
100.0%

Most frequent character per category

ValueCountFrequency (%)
335256
50.0%
435256
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common70512
100.0%

Most frequent character per script

ValueCountFrequency (%)
335256
50.0%
435256
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70512
100.0%

Most frequent character per block

ValueCountFrequency (%)
335256
50.0%
435256
50.0%

Unnamed: 250
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
EUROPE AU SERVICE PEUPLES
35256 

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters881400
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEUROPE AU SERVICE PEUPLES
2nd rowEUROPE AU SERVICE PEUPLES
3rd rowEUROPE AU SERVICE PEUPLES
4th rowEUROPE AU SERVICE PEUPLES
5th rowEUROPE AU SERVICE PEUPLES
ValueCountFrequency (%)
EUROPE AU SERVICE PEUPLES35256
100.0%
2021-02-18T22:27:10.383535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:10.433104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
au35256
25.0%
service35256
25.0%
peuples35256
25.0%
europe35256
25.0%

Most occurring characters

ValueCountFrequency (%)
E211536
24.0%
U105768
12.0%
P105768
12.0%
105768
12.0%
R70512
 
8.0%
S70512
 
8.0%
O35256
 
4.0%
A35256
 
4.0%
V35256
 
4.0%
I35256
 
4.0%
Other values (2)70512
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter775632
88.0%
Space Separator105768
 
12.0%

Most frequent character per category

ValueCountFrequency (%)
E211536
27.3%
U105768
13.6%
P105768
13.6%
R70512
 
9.1%
S70512
 
9.1%
O35256
 
4.5%
A35256
 
4.5%
V35256
 
4.5%
I35256
 
4.5%
C35256
 
4.5%
ValueCountFrequency (%)
105768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin775632
88.0%
Common105768
 
12.0%

Most frequent character per script

ValueCountFrequency (%)
E211536
27.3%
U105768
13.6%
P105768
13.6%
R70512
 
9.1%
S70512
 
9.1%
O35256
 
4.5%
A35256
 
4.5%
V35256
 
4.5%
I35256
 
4.5%
C35256
 
4.5%
ValueCountFrequency (%)
105768
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII881400
100.0%

Most frequent character per block

ValueCountFrequency (%)
E211536
24.0%
U105768
12.0%
P105768
12.0%
105768
12.0%
R70512
 
8.0%
S70512
 
8.0%
O35256
 
4.0%
A35256
 
4.0%
V35256
 
4.0%
I35256
 
4.0%
Other values (2)70512
 
8.0%

Unnamed: 251
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
UNE EUROPE AU SERVICE DES PEUPLES
35256 

Length

Max length33
Median length33
Mean length33
Min length33

Characters and Unicode

Total characters1163448
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUNE EUROPE AU SERVICE DES PEUPLES
2nd rowUNE EUROPE AU SERVICE DES PEUPLES
3rd rowUNE EUROPE AU SERVICE DES PEUPLES
4th rowUNE EUROPE AU SERVICE DES PEUPLES
5th rowUNE EUROPE AU SERVICE DES PEUPLES
ValueCountFrequency (%)
UNE EUROPE AU SERVICE DES PEUPLES35256
100.0%
2021-02-18T22:27:10.554101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:10.603507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
des35256
16.7%
au35256
16.7%
peuples35256
16.7%
europe35256
16.7%
une35256
16.7%
service35256
16.7%

Most occurring characters

ValueCountFrequency (%)
E282048
24.2%
176280
15.2%
U141024
12.1%
P105768
 
9.1%
S105768
 
9.1%
R70512
 
6.1%
N35256
 
3.0%
O35256
 
3.0%
A35256
 
3.0%
V35256
 
3.0%
Other values (4)141024
12.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter987168
84.8%
Space Separator176280
 
15.2%

Most frequent character per category

ValueCountFrequency (%)
E282048
28.6%
U141024
14.3%
P105768
 
10.7%
S105768
 
10.7%
R70512
 
7.1%
N35256
 
3.6%
O35256
 
3.6%
A35256
 
3.6%
V35256
 
3.6%
I35256
 
3.6%
Other values (3)105768
 
10.7%
ValueCountFrequency (%)
176280
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin987168
84.8%
Common176280
 
15.2%

Most frequent character per script

ValueCountFrequency (%)
E282048
28.6%
U141024
14.3%
P105768
 
10.7%
S105768
 
10.7%
R70512
 
7.1%
N35256
 
3.6%
O35256
 
3.6%
A35256
 
3.6%
V35256
 
3.6%
I35256
 
3.6%
Other values (3)105768
 
10.7%
ValueCountFrequency (%)
176280
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1163448
100.0%

Most frequent character per block

ValueCountFrequency (%)
E282048
24.2%
176280
15.2%
U141024
12.1%
P105768
 
9.1%
S105768
 
9.1%
R70512
 
6.1%
N35256
 
3.0%
O35256
 
3.0%
A35256
 
3.0%
V35256
 
3.0%
Other values (4)141024
12.1%

Unnamed: 252
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.6 KiB
AZERGUI Nagib
35256 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters458328
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAZERGUI Nagib
2nd rowAZERGUI Nagib
3rd rowAZERGUI Nagib
4th rowAZERGUI Nagib
5th rowAZERGUI Nagib
ValueCountFrequency (%)
AZERGUI Nagib35256
100.0%
2021-02-18T22:27:10.724924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:27:10.774421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
nagib35256
50.0%
azergui35256
50.0%

Most occurring characters

ValueCountFrequency (%)
A35256
 
7.7%
Z35256
 
7.7%
E35256
 
7.7%
R35256
 
7.7%
G35256
 
7.7%
U35256
 
7.7%
I35256
 
7.7%
35256
 
7.7%
N35256
 
7.7%
a35256
 
7.7%
Other values (3)105768
23.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter282048
61.5%
Lowercase Letter141024
30.8%
Space Separator35256
 
7.7%

Most frequent character per category

ValueCountFrequency (%)
A35256
12.5%
Z35256
12.5%
E35256
12.5%
R35256
12.5%
G35256
12.5%
U35256
12.5%
I35256
12.5%
N35256
12.5%
ValueCountFrequency (%)
a35256
25.0%
g35256
25.0%
i35256
25.0%
b35256
25.0%
ValueCountFrequency (%)
35256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin423072
92.3%
Common35256
 
7.7%

Most frequent character per script

ValueCountFrequency (%)
A35256
8.3%
Z35256
8.3%
E35256
8.3%
R35256
8.3%
G35256
8.3%
U35256
8.3%
I35256
8.3%
N35256
8.3%
a35256
8.3%
g35256
8.3%
Other values (2)70512
16.7%
ValueCountFrequency (%)
35256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII458328
100.0%

Most frequent character per block

ValueCountFrequency (%)
A35256
 
7.7%
Z35256
 
7.7%
E35256
 
7.7%
R35256
 
7.7%
G35256
 
7.7%
U35256
 
7.7%
I35256
 
7.7%
35256
 
7.7%
N35256
 
7.7%
a35256
 
7.7%
Other values (3)105768
23.1%

Unnamed: 253
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct130
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8074937599
Minimum0
Maximum1631
Zeros33614
Zeros (%)95.3%
Memory size275.6 KiB
2021-02-18T22:27:10.832872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1631
Range1631
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15.16141343
Coefficient of variation (CV)18.77588928
Kurtosis4796.489458
Mean0.8074937599
Median Absolute Deviation (MAD)0
Skewness56.59196358
Sum28469
Variance229.8684572
MonotocityNot monotonic
2021-02-18T22:27:10.934488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033614
95.3%
1479
 
1.4%
2321
 
0.9%
3135
 
0.4%
4115
 
0.3%
578
 
0.2%
649
 
0.1%
839
 
0.1%
736
 
0.1%
928
 
0.1%
Other values (120)362
 
1.0%
ValueCountFrequency (%)
033614
95.3%
1479
 
1.4%
2321
 
0.9%
3135
 
0.4%
4115
 
0.3%
ValueCountFrequency (%)
16311
< 0.1%
10711
< 0.1%
6031
< 0.1%
5191
< 0.1%
5101
< 0.1%

Unnamed: 254
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct118
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.007532902201
Minimum0
Maximum2.8
Zeros33633
Zeros (%)95.4%
Memory size275.6 KiB
2021-02-18T22:27:11.041857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2.8
Range2.8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.06990404344
Coefficient of variation (CV)9.279828886
Kurtosis510.5036937
Mean0.007532902201
Median Absolute Deviation (MAD)0
Skewness19.55576757
Sum265.58
Variance0.00488657529
MonotocityNot monotonic
2021-02-18T22:27:11.140842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033633
95.4%
0.02160
 
0.5%
0.03148
 
0.4%
0.01143
 
0.4%
0.04119
 
0.3%
0.05101
 
0.3%
0.0887
 
0.2%
0.0679
 
0.2%
0.0773
 
0.2%
0.0953
 
0.2%
Other values (108)660
 
1.9%
ValueCountFrequency (%)
033633
95.4%
0.01143
 
0.4%
0.02160
 
0.5%
0.03148
 
0.4%
0.04119
 
0.3%
ValueCountFrequency (%)
2.81
< 0.1%
2.391
< 0.1%
2.332
< 0.1%
2.321
< 0.1%
2.311
< 0.1%

Unnamed: 255
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct188
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01686493079
Minimum0
Maximum7.43
Zeros33616
Zeros (%)95.3%
Memory size275.6 KiB
2021-02-18T22:27:11.248644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7.43
Range7.43
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1705923158
Coefficient of variation (CV)10.11520995
Kurtosis695.852239
Mean0.01686493079
Median Absolute Deviation (MAD)0
Skewness22.94400228
Sum594.59
Variance0.02910173822
MonotocityNot monotonic
2021-02-18T22:27:11.355774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033616
95.3%
0.0392
 
0.3%
0.0582
 
0.2%
0.0470
 
0.2%
0.0770
 
0.2%
0.0669
 
0.2%
0.0258
 
0.2%
0.0857
 
0.2%
0.0955
 
0.2%
0.150
 
0.1%
Other values (178)1037
 
2.9%
ValueCountFrequency (%)
033616
95.3%
0.0140
 
0.1%
0.0258
 
0.2%
0.0392
 
0.3%
0.0470
 
0.2%
ValueCountFrequency (%)
7.431
< 0.1%
6.771
< 0.1%
6.681
< 0.1%
6.391
< 0.1%
6.361
< 0.1%

Correlations

2021-02-18T22:27:11.673508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-18T22:27:14.807446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-18T22:27:17.634460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-18T22:27:19.978217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-18T22:27:21.919331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-18T22:26:00.478324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-18T22:26:12.549065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Code du départementLibellé du départementCode de la communeLibellé de la communeInscritsAbstentions% Abs/InsVotants% Vot/InsBlancs% Blancs/Ins% Blancs/VotNuls% Nuls/Ins% Nuls/VotExprimés% Exp/Ins% Exp/VotN°ListeLibellé Abrégé ListeLibellé Etendu ListeNom Tête de ListeVoix% Voix/Ins% Voix/ExpUnnamed: 25Unnamed: 26Unnamed: 27Unnamed: 28Unnamed: 29Unnamed: 30Unnamed: 31Unnamed: 32Unnamed: 33Unnamed: 34Unnamed: 35Unnamed: 36Unnamed: 37Unnamed: 38Unnamed: 39Unnamed: 40Unnamed: 41Unnamed: 42Unnamed: 43Unnamed: 44Unnamed: 45Unnamed: 46Unnamed: 47Unnamed: 48Unnamed: 49Unnamed: 50Unnamed: 51Unnamed: 52Unnamed: 53Unnamed: 54Unnamed: 55Unnamed: 56Unnamed: 57Unnamed: 58Unnamed: 59Unnamed: 60Unnamed: 61Unnamed: 62Unnamed: 63Unnamed: 64Unnamed: 65Unnamed: 66Unnamed: 67Unnamed: 68Unnamed: 69Unnamed: 70Unnamed: 71Unnamed: 72Unnamed: 73Unnamed: 74Unnamed: 75Unnamed: 76Unnamed: 77Unnamed: 78Unnamed: 79Unnamed: 80Unnamed: 81Unnamed: 82Unnamed: 83Unnamed: 84Unnamed: 85Unnamed: 86Unnamed: 87Unnamed: 88Unnamed: 89Unnamed: 90Unnamed: 91Unnamed: 92Unnamed: 93Unnamed: 94Unnamed: 95Unnamed: 96Unnamed: 97Unnamed: 98Unnamed: 99Unnamed: 100Unnamed: 101Unnamed: 102Unnamed: 103Unnamed: 104Unnamed: 105Unnamed: 106Unnamed: 107Unnamed: 108Unnamed: 109Unnamed: 110Unnamed: 111Unnamed: 112Unnamed: 113Unnamed: 114Unnamed: 115Unnamed: 116Unnamed: 117Unnamed: 118Unnamed: 119Unnamed: 120Unnamed: 121Unnamed: 122Unnamed: 123Unnamed: 124Unnamed: 125Unnamed: 126Unnamed: 127Unnamed: 128Unnamed: 129Unnamed: 130Unnamed: 131Unnamed: 132Unnamed: 133Unnamed: 134Unnamed: 135Unnamed: 136Unnamed: 137Unnamed: 138Unnamed: 139Unnamed: 140Unnamed: 141Unnamed: 142Unnamed: 143Unnamed: 144Unnamed: 145Unnamed: 146Unnamed: 147Unnamed: 148Unnamed: 149Unnamed: 150Unnamed: 151Unnamed: 152Unnamed: 153Unnamed: 154Unnamed: 155Unnamed: 156Unnamed: 157Unnamed: 158Unnamed: 159Unnamed: 160Unnamed: 161Unnamed: 162Unnamed: 163Unnamed: 164Unnamed: 165Unnamed: 166Unnamed: 167Unnamed: 168Unnamed: 169Unnamed: 170Unnamed: 171Unnamed: 172Unnamed: 173Unnamed: 174Unnamed: 175Unnamed: 176Unnamed: 177Unnamed: 178Unnamed: 179Unnamed: 180Unnamed: 181Unnamed: 182Unnamed: 183Unnamed: 184Unnamed: 185Unnamed: 186Unnamed: 187Unnamed: 188Unnamed: 189Unnamed: 190Unnamed: 191Unnamed: 192Unnamed: 193Unnamed: 194Unnamed: 195Unnamed: 196Unnamed: 197Unnamed: 198Unnamed: 199Unnamed: 200Unnamed: 201Unnamed: 202Unnamed: 203Unnamed: 204Unnamed: 205Unnamed: 206Unnamed: 207Unnamed: 208Unnamed: 209Unnamed: 210Unnamed: 211Unnamed: 212Unnamed: 213Unnamed: 214Unnamed: 215Unnamed: 216Unnamed: 217Unnamed: 218Unnamed: 219Unnamed: 220Unnamed: 221Unnamed: 222Unnamed: 223Unnamed: 224Unnamed: 225Unnamed: 226Unnamed: 227Unnamed: 228Unnamed: 229Unnamed: 230Unnamed: 231Unnamed: 232Unnamed: 233Unnamed: 234Unnamed: 235Unnamed: 236Unnamed: 237Unnamed: 238Unnamed: 239Unnamed: 240Unnamed: 241Unnamed: 242Unnamed: 243Unnamed: 244Unnamed: 245Unnamed: 246Unnamed: 247Unnamed: 248Unnamed: 249Unnamed: 250Unnamed: 251Unnamed: 252Unnamed: 253Unnamed: 254Unnamed: 255
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